Study on the Interaction Mechanism of Digital Technology in Promoting Tourism and Cultural Heritage Protection in Rural Revitalization

The so-called digital empowerment refers to giving full play to the role and advantages of digital technology to promote the improvement of management efficiency and management quality [1-2]. The deep integration of digital technology and rural tourism can provide strong technical support for the modernization of rural tourism [3]. In the development process of rural tourism, digital empowerment plays a crucial role [4]. In the context of the continuous promotion of the rural revitalization strategy, the integration of rural tourism and digital technology has deepened, which promotes the improvement of the management level of rural tourism, and at the same time promotes the emergence of new products and new modes of rural tourism [5-6]. In addition, the deep integration of digital technology and rural tourism can not only improve the operational efficiency of rural tourism, but also enhance the satisfaction of tourists, thus promoting the high quality and sustainable development of rural tourism [7-8].

On the other hand, the rapid development of China’s urbanization has caused a great impact on the society, economy and culture of the countryside, and a large number of rural cultural heritage has been destroyed, changed, or even extinguished. Rural cultural heritage records the history of human activities and can reflect the national historical value [9-10]. Strengthening the protection of rural cultural heritage and building a perfect protection system for rural cultural heritage has become a consensus among countries around the world. The United Nations Educational, Scientific and Cultural Organization (UNESCO) believes that community participation is a basic tool for heritage management practices, and that community-based protection of rural cultural heritage is essential for lasting conservation efforts to promote sustainable livelihoods of community residents, emphasizing the integration of heritage conservation with the production and life of local community residents and the need for holistic protection of rural cultural heritage [11-12].

In recent years, the international heritage conservation field has been paying more and more attention to the study of rural cultural heritage, advocating that the protection of rural cultural heritage should be integrated with the development of the productive life of the community residents in the heritage site [13]. The comprehensive popularization of the Internet and digital technology has provided a new platform and technical support for the protection and utilization of rural cultural heritage [14]. However, current digital research on rural cultural heritage mainly focuses on the heritage ontology, often ignoring the connection between heritage protection and community development, making it difficult to utilize the advantages of Internet technology in heritage experience and digital communication, which is not conducive to the sustainable development of the communities in heritage sites [15-16].

Digital empowerment plays a crucial role in the upgrading process of rural tourism industry. Literature [17] used semi-structured interviews to examine the process of digital transformation of regional empowerment rural tourism, and there is an interest in the formation of the academic pattern of research on the digital development of rural tourism, especially on the research on the elements of community empowerment in rural tourism. Literature [18] provides an in-depth analysis of the nature of digital transformation of rural tourism, as well as the obstacles and opportunities faced in the digital transformation process, and argues that tailored policies and strategies need to be implemented across sectors related to rural tourism to facilitate the participation of rural SMEs in the digital transformation process. Literature [19] examined the use of electronic folklore images on rural tourism websites and found that all digital copies contributed to the process of digital ecosystem integrity. Literature [20] combined digital social media content as well as online comments to discuss that online social media effectively improved people’s attitudes towards rural tourism exploiting indigenous and local cultures, deepened scholars’ knowledge and understanding of rural tourism, and also pointed out that digital media broadened the channels of attracting tourists to rural tourism to a certain extent. Literature [21] examined the current situation of rural tourism business, pointed out that information and communication technology is conducive to the rural tourism business process of business difficulties and business risk reduction, and promote the development of rural tourism industry. Literature [22] introduced the concept of digital economy and the theoretical basis of digital economy, and empirically explored the development of rural tourism based on the product matching model, pointed out that the digitalization index significantly affects the entrepreneurial activities of rural tourism, and proposed that the future rural development policy can focus on the establishment of rural innovation environment. The scholars’ research reveals the positive effects of digital technology on rural tourism, including policy, attraction, entrepreneurial environment, and digital ecology construction, so it is necessary to further explore the potential of digital technology in the tourism industry.

The innovative use of digital technologies to protect and manage rural cultural heritage resources has become a new trend in the field of heritage conservation. Literature [23] conducted an exploratory study to illustrate the digital rural cultural heritage strategy to connect the physical and digital dimensions of rural cultural heritage tourism products, and to strengthen the intrinsic value of cultural heritage tourism products as well as the perceptual experience. Literature [24] emphasizes the importance of reconnecting with the cultural heritage of farms and fostering a sense of belonging, and tries to examine how digital technology can promote rural tourism development, using three real-life cases to develop a detailed discussion. Literature [25] analyzes the impact of digital heritage projects on large-scale rural landscapes in the United States, pointing out that digital technology effectively preserves the original archival materials of natural and cultural landscapes and avoids damage and loss. Literature [26] introduces the rurAllure project, which aims to use digital information technology to develop a new model of mobile tour guide, which can display the cultural heritage and related cultural activities, transportation, accommodation and other information for tourists on their pilgrimage. Literature [27] discusses that cultural heritage has the function of promoting social inclusiveness, and examines the value and role that digital technology can play in the field of cultural heritage by combining interviews and survey experimental methods, making a positive reference to the cooperation between cultural heritage and human-computer interaction, information science, and other fields. Digital technologies have been effective in preserving, innovating and attracting traffic to cultural heritage, but the cultural heritage enabled by digital technologies is still in the initial stage, and there is still a huge potential for the practice of digital technologies in cultural heritage.

The study establishes an evaluation index system for tourism and cultural heritage protection based on four dimensions: scale of operation, reception capacity, level of development and quality of development, and two dimensions of tourism and cultural heritage protection respectively. At the same time, it builds an evaluation index system for the development of digital technology, covering the four dimensions of infrastructure, industrial digitization, digital industrialization, and digital environment. Using the 2010-2022 time series data of a certain place, the cloud model is used to conduct an empirical analysis to assess the development level of tourism and cultural heritage protection and the development level of digital technology in the place. On this basis, explanatory variables, interpreted variables and control variables are selected, a research model is set, and a regression model is used to explore the impact of the dimensions of digital technology development on tourism and cultural heritage protection, and to explore the interactive mechanism of digital technology to promote tourism and cultural heritage protection.

2

Evaluation index system construction and measurement

This chapter explores the development status of tourism and cultural heritage protection and digital technology by constructing an indicator system for the development of digital technology and using a cloud model for empirical analysis, in order to prepare for the subsequent exploration of the interaction mechanism between digital technology and tourism and cultural heritage protection.

2.1

Construction of the indicator system

This paper follows the principles of systematicity, scientificity and objectivity to construct the indicator system of tourism and cultural heritage protection and development, the four dimensions are the scale of operation, reception capacity, level of development and quality of development, and the AHP method is used to complete the assignment of indicators. The indicator system of tourism and cultural heritage protection and development is shown in Table 1. The indicator system of tourism and cultural heritage protection and development includes the number of enterprises and the number of employees, the number of visits and the number of employees, the business income, the proportion of the industry in the tertiary industry and the proportion of the industry in the gross domestic product (GDP). Among them, the revenue from domestic tourism (0.089) and the business income of cultural market operators (0.086) have the largest weights.

Table 1.

The index system of tourism and cultural heritage protection development

General indicator	Primary indicator	Secondary indicator	Weight
Cultural heritage protection development	Operating scale	Cultural and related industry agencies	0.038
	Operating scale	The number of people working in culture and related industries	0.030
	Receptivity	Museum circulation	0.070
		The art exhibition group performed the audience	0.025
		The reference of the cultural relics industry	0.064
	Development level	The income of art performance	0.058
		The revenue of the cultural market operation mechanism	0.086
		Cultural revenue	0.061
	Development quality	The added value of the cultural industry accounts for the increase of the third industry	0.038
	Development quality	The added value of the cultural industry accounts for the proportion of GDP	0.047
Tourism development	Operating scale	Number of travel enterprises	0.037
	Operating scale	Number of travel professionals	0.061
	Receptivity	Number of tourists in the country	0.065
	Receptivity	Number of inbound visitors	0.063
	Development level	Domestic travel income	0.089
	Development level	Inbound tourism income	0.072
	Development quality	Tourism revenue accounts for the added value of the third industry	0.046
	Development quality	Tourism revenue for the proportion of GDP	0.051

In order to measure the level of digital technology development more scientifically, the evaluation system of digital technology development will be constructed by adopting four first-level indicator systems: infrastructural construction, industrial digitization, digital industrialization and digital environment. The indicator system and weights of digital technology development are shown in Table 2. The first-level indicator of infrastructure construction includes three secondary indicators: the total number of Internet users, the Internet penetration rate, and the number of Internet access users. The first-level indicators of industrial digitization include two second-level indicators of e-commerce sales and the number of enterprises with e-commerce trading activities. The first-level indicator of digital industrialization includes two secondary indicators: the total amount of postal and telecommunications business and the total amount of income from software business. The first-level indicator of digital environment includes four secondary indicators: the number of Internet-related employees, the number of employees in the information service industry, the total amount of technology contract transactions, and the number of authorized patent applications. The number of employees in the information service industry and the number of enterprises with e-commerce trading activities have the largest weights, 0.118 and 0.117 respectively.

Table 2.

The index system and weight of digital technology development

General indicator	Primary indicator	Secondary indicator	Weight
Digital technology development level	Infrastructure	Number of Internet users	0.073
		Internet penetration	0.098
		Internet access users	0.065
	Industry digitization	E-commerce sales	0.108
	Industry digitization	The number of enterprises with e-commerce trading activities	0.117
	Digital industrialization	The total amount of postal services	0.104
	Digital industrialization	Total revenue of software industry	0.098
	Digital environment	The number of Internet professionals	0.088
		The number of information service industries	0.118
		The total amount of the technical contract	0.069
		Number of patent applications	0.062

2.2

Measurement methods

In this study, from the selection of evaluation indexes to the calculation of weight coefficients, as well as the final generation of comprehensive evaluation, there is a certain degree of ambiguity and uncertainty, and there is a certain degree of randomness, especially the uncertainty and randomness generated by the process of expert evaluation, and the evaluation results obtained by the fuzzy comprehensive evaluation method can not reflect the problem of uncertainty and randomness. The cloud modeling method can not only better deal with the fuzzy nature of the evaluation index concept, but also deal with the uncertainty and randomness caused by the non-uniform understanding of the concept by different experts in the evaluation scoring process. It can also simulate expert evaluation results through cloud droplet simulation, which better solves the problem of limited sample data in evaluation and makes the evaluation results more reliable. In summary, combined with the characteristics of the evaluation system and the research process, this paper adopts the cloud model method as the evaluation mathematical model of tourism and cultural heritage protection and the level of development of digital technology.

2.2.1

Basic concepts

Cloud modeling, based on probability theory and fuzzy mathematics, allows for the conversion of qualitative concepts into quantitative data. Its advantage lies in dealing with problems that combine vagueness, randomness, and uncertainty.

Let U be a quantitative thesis expressed using exact values, and the elements in U be denoted by x. Y is a qualitative concept on the quantitative domain U, and the distribution of μ(x) over the domain U is said to be a cloud if x is a random number with a stable tendency to the determinism μ(x) ∈ [0,1] of C. Each x is called a cloud droplet, denoting one quantitative realization of the qualitative concept by a cloud droplet on the domain U.

2.2.2

Digital features

Numerical characterization of cloud models refers to the adoption of numbers i.e., representing the overall characteristics of the cloud model concepts, through three eigenvalues: expectation, entropy, and super-entropy, which together achieve qualitative and quantitative transformation of uncertainty concepts.

Expectation Ex is the center of gravity of the cloud droplet and represents the expected value of the distribution of qualitative concepts in the domain space, i.e., the most probable point of a qualitative concept in a given domain space.

Entropy En is used to measure the ambiguity and randomness of a qualitative concept. The size of the En value represents the range of values that can be accepted for a qualitative concept. The larger the En value, the broader the qualitative concept, and the greater the randomness and vagueness of the concept.

Hyperentropy He measures the discrete degree of entropy En, representing the cohesiveness of each numerical affiliation belonging to a certain qualitative concept, which is a quantitative representation of the discrete degree and thickness of the cloud. The larger the value of superentropy He, the greater the degree of discrete and thickness of the cloud.

2.2.3

Cloud generators

Cloud generators are tools that enable the conversion between qualitative and quantitative information as a mapping relationship and are categorized into forward and inverse generators, among others. Forward cloud generators produce eligible cloud droplets by inputting known numerical features (Ex, En, He) of the cloud model.

Input: a numerical feature Ex,En,He reflecting the qualitative concept and set the number of generated cloud drops to N.

Output: N cloud droplets x_i with their quantitative values μ_i.

Positive cloud generator specific algorithm:

In the first step, generate a normal random number En′ ~ N(En,He²) with En as expectation and He² as variance.

Second step, generate normal random number x_r ~ N(Ex,En^′2) with Ex as expectation and En^′2 as variance.

Step 3, Calculate: (1) $μ (x_{i}) = e^{- {(x_{i} - E x)}^{2} / 2 E n^{2}}$

Then any (x_i,y_i) represents a cloud droplet, thus realizing the conversion from qualitative to quantitative concepts.

In the fifth step, the first to fourth steps are repeated until the set number of cloud droplets is generated.

The inverse cloud generator is the inverse process of the forward cloud generator, in which a known number of cloud droplets are converted to obtain the numerical feature (Ex,En,He) of the cloud, which achieves the description of the qualitative concept.

Input: N cloud droplet x_i.

Output: digital features representing N qualitative concepts of cloud droplets Ex,En,He.

Inverse cloud generator specific algorithm:

In the first step, input n samples x_i and calculate the sample mean $\bar{X}$ and sample variance S² of this set of sample data with the following formula: (2) ${\begin{array}{l} \bar{X} = \frac{1}{n} \sum_{i = 1}^{n} x_{i} \\ S^{2} = \frac{1}{n - 1} \sum_{i = 1}^{n} {(x_{i} - \bar{X})}^{2} \end{array}$

In the second step, the cloud model digital feature expectation $E x = \bar{X}$ is calculated.

In the third step, calculate the cloud model digital feature entropy En with the following formula: (3) $E n = \sqrt{\frac{π}{2}} \cdot \frac{1}{n} \cdot \sum_{i = 1}^{n} | x_{i} - \bar{X} |$

In the fourth step, the cloud model digital feature superentropy He is calculated with the following equation: (4) $H e = {(S^{2} - E_{n}^{2})}^{\frac{1}{2}}$

2.3

Measurement of the level of development

This paper selects the data related to the cultural and tourism industry and digital economy of a place from 2010 to 2022 to carry out an empirical study, and uses the cloud model to measure the development level of its tourism and cultural heritage preservation and digital technology, respectively.

2.3.1

Tourism and cultural heritage preservation and development

The development index of tourism and cultural heritage protection in the sample area is shown in Figure 1. From 2010 to 2019, the development level of tourism and cultural heritage protection in the sample area increased significantly. The development index of tourism and cultural heritage protection in the sample area reached a maximum value of 0.899 in 2019, and from 2019 to 2022 the development index of tourism and cultural heritage protection’s development index shows a decreasing trend, and the average value of the development index for 2010-2022 as a whole is 0.518, which is at a medium level.

By analyzing the development index of cultural heritage protection, it can be seen that the development level of cultural heritage protection in the sample area was in a steady increase until 2017, began to decline in 2018, recovered in 2019, fell straight down in 2020, and was in a slow recovery stage in 2021-2022. The raw data show that the number of cultural and related industry organizations and employees, the revenue of art performance venues and group performances, and the business revenue of cultural market operators all declined significantly in 2018.

The development level of the tourism industry in the sample area has been in a fast-growing trend from 2010 to 2019. Under the vigorous promotion and guidance of national policies, governments at all levels have attached great importance to the development of the tourism industry, infrastructure and service facilities have been increasingly improved, market consumption demand has been growing, and the level of tourism management and service has been improved, which has contributed to the rapid and prosperous development of the tourism industry.

2.3.2

Digital technology development

Figure 2 shows the digital technology development index of the sample area. The development of digital technology in the sample area shows an increasing trend from year to year, reaching a maximum value of 0.881 in 2022, and the average value of the development index in the study area is 0.499, which is a medium level of development.

2.3.3

Correlation analysis

The correlation analysis of tourism and cultural heritage protection and digital technology development is shown in Figure 3. The comprehensive development level of tourism and cultural heritage protection in the sample area in 2010-2019 is generally on an upward trend, and only a small decline occurs in 2018, mainly because of the decline in the development level of the cultural industry. In contrast, driven by the rapid development of digital technology and the national digital transformation strategy, the development of digital technology is in a sustained growth trend, especially in 2020-2022, when tourism and cultural heritage protection suffered a heavy blow to appear a straight line decline, the development of digital technology, on the contrary, appeared to be a relatively large increase.

Through the correlation analysis of tourism and cultural heritage protection and digital technology development index, it can be seen that the correlation between the two is 0.625, and it has passed the significance test, that is, there is a positive correlation between tourism and cultural heritage protection and digital technology development, and digital technology provides new ideas, new formats and new space for the high-quality development of tourism and cultural heritage protection, and its rapid development can promote the integrated development of cultural and tourism industry.

3

Interactive mechanisms of digital technology for cultural and tourism development

After the previous measurement and correlation analysis of the development level of tourism and cultural heritage protection and digital technology, it can be seen that the correlation between tourism and cultural heritage protection and digital technology is initially verified. Next, by combining the regression analysis method, its specific interaction mechanism is explored.

3.1

Regression analysis methods

In multiple regression analysis, when the dependent variable y is interfered by external influencing factors, these influencing factors are defined as independent variable x_i . If there is a certain correlation between y and x_i , a multiple regression model can be established based on the dependent variable y and each influencing factor x_i, and the mathematical expression of the model is as follows: (5) $y = b_{0} + b_{1} x_{1} + b_{2} x_{2} ... + b_{n} x_{n} + ε$

In the above equation, b_n is the regression coefficient and ε is the random error.

3.1.1

Significance test

The modeling of multiple linear regression models requires the testing of the following coefficients:

1)

Significance test of regression equation

In many practical problems, the relationship between dependent variable y and independent variable x_i is not obvious, and it needs to be tested and verified by certain significance tests to finally determine whether there is a good and significant relationship between the two. If they do not have good significance, it means that there is no relevant connection between y and x_i.

To test the significance of the regression equation, the statistic F is generally quoted as the constraint of the model, and the expression of the statistic F is: (6) $F = \frac{s_{Back} / P}{s_{Leftover} / (n - p - 1)}$

In the above equation, s_Back is the regression sum of squares, s_Leftover is the residual sum of squares, and the statistic F should obey a F(p,n – p – 1) distribution, i.e., obey the level of significance α, α can be determined by the following expression: (7) $p {| F | \geq F_{1 - α, p, n - p - 1} | H_{0} | = α}$

The above formula is the expression of significance test, if it satisfies |F|≥ F_{1–α,p,n–p–1}, it means that there is a good significance relationship between y and x_i at α significant level, and the constructed model is good in significance.

2)

Significance test of regression coefficients

In the multiple regression model, the regression equation is significant does not mean that each independent variable is significant for the dependent variable, at this time, we should analyze the significance of the independent variables, keep the independent variables with high significance, and eliminate the insignificant independent variables, so as to avoid the model significance being affected. After eliminating the irrelevant influencing factors, the parameters of the independent variables of the model can be optimized, so that we can study the intrinsic connection between y and x_i more precisely, and also analyze the deformation of the dependent variable better.

If the effect and influence of a variable x_i on the dependent variable y is not very significant, the value of coefficient β_j of the multiple regression model should be 0, and the test of whether the dependent variable x_i is significant or not can often be verified by the following expression: (8) $\frac{β_{j}^{2} / c_{j j}}{S_{Ê £} / (n - p - 1)} - F (1, n - p - 1)$ $${{\beta _j^2/{c_{jj}}} \over {{S_{{Ê} {£}}}/(n - p - 1)}} - F(1,n - p - 1)$$

In the above equation, if independent variable x_i is significant for dependent variable y, it obeys a F(1,n – p – 1) distribution. $β_{j}^{2} / C_{j j}$ is the partial regression sum of squares of the independent variable x_i, and the regression coefficient β_j is considered significant at the confidence interval of 1 – α if |F|≥ F_{1–α,p,n–p–1}.

However, it is worth noting that when uncorrelated variables are eliminated from the multiple regression equation, the coefficients b_n of the remaining x_i also change, so the multiple regression equation has to be re-established by eliminating the variables once, and then each coefficient in the newly established multiple regression equation is tested one by one until all the regression coefficients are significant.

3.1.2

Estimation of regression parameters

In the multiple regression model, the regression parameters, also known as regression coefficients, for the multiple model, the smaller the deviation value between the observed value and the estimated value of the model, the higher the fitting accuracy of the model and the best prediction, which requires the least squares rule to find the estimated value of each regression coefficient b₀,b₁,…b_n, and ultimately determine the expression of the multiple regression model.

Let Δ_i be the deviation of the observed value y_i and the estimated value ŷ_i, then: (9) $Δ_{i} = y_{i} - {\hat{y}}_{i}$

At the same time: (10) $y = (\begin{matrix} y_{1} \\ y_{2} \\ ... \\ y_{n} \end{matrix}) Δ = (\begin{matrix} Δ_{1} \\ Δ_{2} \\ ... \\ Δ_{n} \end{matrix}) x = (\begin{matrix} x_{1} \\ x_{2} \\ ... \\ x_{n} \end{matrix}) \hat{b} = (\begin{matrix} b_{0} \\ b_{1} \\ ... \\ b_{n} \end{matrix})$

In the above equation x is the various types of influences, $\hat{b}$ the estimates of the regression parameters, and y the observations.

Let the residual of y be r, then we have: (11) $r = \hat{b} x - y$

Requested: (12) $\frac{\partial r^{T} r}{\partial \hat{b}} = 2 r^{T} x = 0$

Substituting equation (12) into equation (11), we have: (13) $x^{T} x \hat{b} = x^{T} y$

After arithmetic, the estimate of b_n is obtained as: (14) $\hat{b} = {(x^{T} x)}^{- 1} x^{T} y$

After finding the regression parameter $\hat{b}$ , the multiple regression model is obtained as: (15) $y = {\hat{b}}_{0} + {\hat{b}}_{1} x_{1} + {\hat{b}}_{2} x_{2} ... + {\hat{b}}_{n} x_{n} + ε$

3.2

Modeling

In order to explore the impact of digital technology to promote the development of tourism and cultural heritage protection, the development index of tourism and cultural heritage protection in the model is set as the explanatory variable, the level of digital technology development is set as the core explanatory variable, and the control variables are controlled through the data of some macroeconomic variables such as GDP, economic growth rate, and the related cultural and tourism industry development variables such as the fixed investment in cultural and related industries. Selection, set regression model.

3.3

Description of variables

1)

Explained variables. Tourism and Cultural Heritage Protection (TCHP), which is represented by the development index of tourism and cultural heritage protection in the sample area derived from the industrial synthesis of the comprehensive level of cultural heritage protection and the comprehensive level of tourism industry.

2)

Core explanatory variables. The level of digital technology development (DT) mainly includes four dimensions, namely, infrastructural construction (IC), digital industrialization (DI), industrial digitization (ID), and digital environment (DE), and the dimensional indicator system is established separately to analyze the impact of digital technology on the development of tourism and cultural heritage protection.

3)

Control variables. ① Gross regional product (GDP), expressed as the logarithm of the sample GDP. ② Gdprate is expressed as the growth rate of regional GDP. ③ Invest is the logarithm of fixed asset investment in culture and related industries. ④ Cultureconsume is the logarithm of per capita consumption expenditure on culture and entertainment. Income is the logarithm of tourism income in the sample area. Tourismconsume is the logarithm of per capita daily spending by overnight visitors. The data for control variables are mainly obtained from relevant official statistical websites.

The descriptive statistics of each variable are shown in Table 3. The development index of tourism and cultural heritage protection ranges from 0.247 to 0.899, and the development index of digital technology ranges from 0.302 to 0.881.

Table 3.

Descriptive statistics result of variables

VarName	Mean	SD	Min	Median	Max
TCHP	0.518	0.024	0.247	0.553	0.899
DT	0.499	0.037	0.302	0.439	0.881
IC	0.047	0.015	0.001	0.114	0.253
DI	0.031	0.049	0.008	0.125	0.216
ID	0.057	0.045	0.009	0.134	0.172
DE	0.042	0.028	0.004	0.127	0.261
LnGDP	9.247	0.042	6.788	8.859	12.041
Gdprate	8.756	0.046	1.788	10.022	20.984
LnInvest	15.635	1.028	10.242	14.606	18.455
LnCultureconsume	6.395	0.023	3.887	5.254	8.721
LnIncome	7.243	1.019	3.559	6.951	10.159
LmTourismconsume	5.308	0.051	2.894	5.428	8.531

3.4

Empirical test results

The fixed-effect model was used to obtain regression results for the level of digital technology development and the four dimensions of tourism and cultural heritage protection. The regression results of the model are shown in Table 4, and (1) to (5) are the regression results of the development level of digital technology and the four major dimensions on tourism and cultural heritage protection, respectively. Digital technology has a positive and significant impact on tourism and cultural heritage protection at a statistical level of 1%, with an impact coefficient of 0.541, confirming that digital technology significantly promotes the development of tourism and cultural heritage protection. The intelligent application of modern digital technology improves cultural production efficiency and reduces production costs, while also increasing the development process of tourism and cultural heritage protection through digital innovation.

Table 4.

Regression result of model

	(1) TCHP	(2) TCHP	(3) TCHP	(4) TCHP	(5) TCHP
DT	0.541***
DT	6.684
IC		0.473***
IC		3.438
DI			0.826***
DI			8.854
ID				0.253
ID				0.896
DE					0.916***
DE					7.899
LnGDP	0.066**	0.098***	0.071**	0.146***	0.075***
LnGDP	3.329	4.425	3.705	2.482	2.996
Gdprate	-0.0002	-0.0001	-0.0003	-0.0002	-0.0004
Gdprate	-1.569	-1.404	-1.059	-0.309	-1.277
LnInvest	0.000**	0.014**	0.011***	0.012**	0.010**
LnInvest	1.797	2.585	2.655	1.747	3.225
LnCultureconsume	0.022***	0.023***	0.023***	0.018**	0.021**
LnCultureconsume	2.681	1.809	2.681	2.625	3.608
LnIncome	0.003	-0.014	0.007	-0.017*	0.007
LnIncome	0.551	-1.737	1.372	-2.335	0.968
LmTourismconsume	0.021	0.015	0.003	-0.017	0.022
LmTourismconsume	0.742	0.166	0.019	-0.143	0.373
_cons	-0.682*	-0.745*	-0.653*	-0.763*	-0.809*
_cons	-2.446	-2.424	-1.926	-2.481	-1.978
adj.R²	0.825	0.812	0.803	0.795	0.819
r²	0.851	0.831	0.816	0.832	0.841
Year	Control	Control	Control	Control	Control
F	80.691	72.375	85.877	61.787	81.206

From the perspective of each subdimension of digital technology, infrastructure has a positive and significant effect on tourism and cultural heritage protection at the 1% statistical level (0.473). Scientific and technological innovation is an important driving force for the development of the digital cultural tourism industry, and the new infrastructure provides solid technical support for the development of the digital cultural tourism industry, and improving the manufacturing level of digital equipment is the key to tapping the development potential of the digital ask rate.

Digital industrialization has a positive and significant effect on tourism and cultural heritage protection at the 1% statistical level (0.826). The digital industry provides culture and tourism with more convenient information transmission and network energy to play with, and utilizes all kinds of new industries to promote the integrated development of culture and tourism.

The impact of industrial digitization on tourism and cultural heritage protection is not significant. As the pace of industrial digitization accelerates, all kinds of new digital applications continue to emerge, and the corresponding cultural and tourism market supervision and laws and regulations have not yet adjusted to the new challenges.

The digital environment has a positive and significant impact on tourism and cultural heritage protection at the 1% statistical level (0.916), and is the digital technology dimension with the greatest impact on tourism and cultural heritage protection. The digital environment is mainly reflected in the support of talent and technology, and digital technology permeates all aspects of the development, production, experience and management of the cultural and tourism industry, promoting the continuous extension of the cultural and tourism industry chain and enriching the ecosystem of the cultural and tourism industry. New industrial practices have raised the requirements of digital cultural and tourism talents in terms of professional knowledge reserves, modern information technology application capabilities, and comprehensive professionalism. By means of improving online training capacity, upgrading the functions of online platforms and strengthening digital marketing, it has responded to the new trend of the development of the cultural and tourism industry in the context of the digital economy.

4

Paths of digital technology to promote tourism and cultural heritage protection

The analysis in the previous chapter shows that digital technology has a better role in promoting tourism development and protecting cultural heritage in rural revitalization. On this basis, this chapter proposes the development path of digital technology to promote tourism and cultural heritage protection. The development path of digital technology to promote tourism and cultural heritage protection is shown in Figure 4, which is divided into four dimensions.

4.1

Upgrading digitization

Comprehensive use of regional cultural resource advantages, through the establishment of a digital tourism platform, the development of digital cultural heritage database and other ways can effectively improve the digital level of rural cultural tourism and creative industries. First of all, the digital tourism platform can provide real-time tourism information, attraction introduction, travel route recommendation, etc., to help tourists better understand and experience rural culture. At the same time, it can continuously optimize the quality of rural tourism services and improve the satisfaction of tourists through user evaluation and feedback. Secondly, through the digital cultural heritage database, the cultural heritage of the countryside can be comprehensively recorded and preserved, which not only helps to protect and pass on the rural culture, but also explores more cultural values. In addition, the advantages of regional cultural resources are comprehensively utilized and transformed into attractive cultural and tourism products through digital technology, such as virtual reality experiences, online exhibitions, cultural education, etc., so as to attract more tourists to come and experience. Finally, the government and related organizations should increase their investment and support for digital technology to create more innovative and attractive cultural and tourism products to promote rural tourism development and cultural heritage protection.

4.2

Developing digital talent

Cultivate digital talents in rural cultural tourism and creative industries and establish a talent exchange platform. First, develop training courses. Local governments or relevant institutions organize regular training courses to teach the basic knowledge and application of digital technology, and provide skill reserves for young talents who return to their hometowns to start their own businesses. Second, attract young talents to return to their hometowns and start businesses. Through policy guidance and entrepreneurial support, young talents with digital technology backgrounds are attracted to return to their hometowns and devote themselves to rural cultural tourism and cultural and creative industries. Build a digital talent exchange platform through which relevant enterprises can release recruitment information, find suitable talent, and promote technological innovation and industry upgrading. At the same time, the platform provides market research and data analysis services to help enterprises better understand market demand and improve the quality and efficiency of products and services. The platform can also provide data support and decision-making reference for the government, promote the government’s strategic planning and policy making in rural tourism development, and realize the benign interaction between rural tourism and local economic development.

4.3

Optimizing the digital environment

Optimize the digital environment for the development of rural tourism and the protection of cultural heritage. First, strengthen the construction of network infrastructure. Expand Internet coverage and increase network bandwidth and speed in rural areas to ensure fast and stable network connections, facilitate better access to and sharing of cultural tourism and cultural heritage information among rural residents and tourists, and improve the service quality and visibility of cultural tourism and cultural heritage industries. Secondly, to increase Internet penetration in rural areas. By providing more digital facilities and services, such as public Wi-Fi hotspots and digital cultural experience facilities, further improving the digitalization level in rural areas will facilitate the online promotion and marketing of rural cultural tourism and creative industries. Third, promote digital applications and services. Encourage the development of applications and services adapted to the needs of rural cultural tourism and cultural and creative industries, such as smart guide systems, online booking platforms, virtual experience tools, etc., in order to enhance the convenience, interactivity and experience of rural tourism. Fourth, training and education. Enhancing the digital skills and literacy of rural residents through digital technology training and education will help expand employment opportunities and improve the income and living standards of rural residents.

4.4

Strengthening digital innovation efforts

Strengthening digital innovation efforts in rural tourism development and cultural heritage protection. On the one hand, it is necessary to deeply explore regional cultural characteristics to form a unique IP brand. It is necessary to grasp the local history and culture, folk culture, natural landscape and other regional characteristics, and integrate them into the development of cultural tourism and creative products to form a unique rural cultural tourism and creative IP. On the other hand, it is necessary to use digital technology extensively to develop innovative products. Grasp the development trend of digital technology and apply it to the rural cultural tourism and creative industry to develop more innovative products. For example, virtual reality, augmented reality, and other technologies can be used to provide tourists with an immersive tourism experience. Big data analysis can be used to accurately grasp the consumption habits and needs of tourists and provide them with better services.

5

Conclusion

Digital technology-enabled cultural tourism development is both an important way of rural revitalization and cultural tourism development, as well as an important topic for cultural tourism research. On the basis of constructing the index system of tourism and cultural heritage protection and digitalization development level respectively, this study uses a cloud model to measure the tourism and cultural heritage protection development index and digitalization technology development index of a place from 2010 to 2022. On this basis, regression analysis is applied to explore the interaction mechanism between the two.

The overall index of tourism and cultural heritage protection in the sample area in 2010-2022 is 0.518, while the index of digital technology development is 0.499, both of which are at the middle development level. Tourism and cultural heritage protection shows a trend change of increasing and then decreasing, while digital technology development is an increasing trend year by year, and the maximum value of both development indexes appears in 2019 (0.899) and 2022 (0.881), respectively. Meanwhile, the correlation between tourism and cultural heritage protection and the development of digital technology is 0.625, which preliminarily determines the promotion effect of digital technology on tourism and cultural heritage protection.

After regression analysis, digital technology has a positive and significant effect on tourism and cultural heritage protection at the 1% level, with an impact degree of 0.514. Specifically, the order of the impact degree of the dimensions of digital technology on tourism and cultural heritage protection is as follows: digital environment (0.916), digital industrialization (0.826), and infrastructural construction (0.473). Digital technology provides strong technical support for the development of tourism and cultural heritage protection.

In the future, efforts need to be made to improve the relatively backward status quo of network coverage and hardware facilities in rural areas, to cultivate a team of digital talents with certain technical skills and innovative thinking, to give full consideration to the protection and inheritance of cultural resources, to avoid damaging rural culture, and to strengthen the network security guarantee and technical precautionary measures, so as to escort the revitalization and development of the countryside.

Langue:: Anglais

Périodicité:: 1 fois par an
Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

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Study on the Interaction Mechanism of Digital Technology in Promoting Tourism and Cultural Heritage Protection in Rural Revitalization

Bo Wang

Publié en ligne: 17 mars 2025

Reçu: 08 oct. 2024

Accepté: 02 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0275

Mots clésEvaluation index system, Cloud model, Development level measurement, Regression analysis, Interaction mechanism, Tourism and cultural heritage protection

© 2025 Bo Wang, published by Sciendo

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

Mots clés
Evaluation index system, Cloud model, Development level measurement, Regression analysis, Interaction mechanism, Tourism and cultural heritage protection