Visitor education as knowledge service: Impact on socioeconomic development and tourism industry

Visitor education stands as a pivotal element of managing, conserving natural and cultural resources. Visitors can have a significant influence on the environment, especially in sensitive areas such as national parks, wildlife reserves, and historic sites. Educating visitors on responsible behavior can mitigate their impact, preserve the natural and cultural resources for future generations. Providing visitors with educational information about the resources they are visiting can enhance their experience and understanding of the place. This can lead to increased appreciation and support for conservation efforts. Some natural and cultural resources can be dangerous if not carefully handled or understood adequately. Educating visitors about safety guidelines and precautions can help prevent accidents and ensure a safe visit for everyone. Sustainable tourism involves meeting the needs of visitors while minimizing negative impacts on the environment, culture, and local economy. Educating visitors about sustainable practices can help promote responsible tourism and contribute to the long-term viability of the resource. Many resources bear cultural significance to local communities. Educating visitors about the cultural context of the resource can foster understanding and respect for local traditions and customs. Wei D. and Wen S. proposed that visitor education is essential to managing and conserving natural and cultural resources by promoting responsible behavior, enhancing experiences, ensuring safety, promoting sustainable tourism, and understanding cultural sensitivity.

In the era of knowledge economy, Davis C and Piva M proposed that knowledge replaces traditional resources such as capital, land and labor force and becomes the dominant resource element [1, 2]. As an informal learning pattern beyond traditional school education, tourism has an important influence on the overall layout of "Five-sphere integrated plan of China" consisting economic construction, political construction, cultural construction, social construction and ecological civilization construction. From the perspective of visitor education, the essence of visitor education activity is a process of knowledge service. The value of knowledge service is the core value of visitor education. The research conducted by Wen S. shows that visitor education activities effectively alter the knowledge, attitude and behavior of tourists. Thus, visitor education can reduce the environmental loss and management cost of tourism destinations [3]. Simultaneously, the dissemination of tourism information dissemination is not only a process of new knowledge generation and diffusion, but also a process of transforming knowledge into new visitor education program. The visitor education is to enhance the visitor experience by providing them with knowledge and understanding about the exhibits, displays, or natural environment they are visiting. Winter P observed through practice that some visitor education projects with plenty knowledge and good service level have become a new economic growth point for many tourism destinations and tour operators [4]. Some scholars such as Dwyer and Forsyth also believe that knowledge is a very special asset, the cost of knowledge learning and diffusion is far lower than the production cost because of its non-exclusiveness, so knowledge has its economies of scale [5, 6]. Therefore, the quantitative estimation of the knowledge service function of tourism resources on different scales has become the key factor to solve the relationship between tourist education activities and social and economic development. Therefore, the quantitative estimation of the knowledge service function of tourism resources on different scales has become the key factor to solve the relationship between tourist education activities and social and economic development. To further clarify the contributions of various authors, we have included a table summarizing their key contributions (see Table 1).

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Table 1. Contributions of analyzed authors.

https://doi.org/10.1371/journal.pone.0310112.t001

Visitor education and knowledge diffusion: A network model for ecological civilization in tourism destinations

In this paper, "civilization" refers to the complex societies with developed urban centers, sophisticated cultures, advanced social organization, and established legal and political norms. "Ecological civilization" extends this concept, emphasizing a sustainable model where economic and social development are balanced with environmental stewardship. Within tourism destinations, ecological civilization manifests as the integration of sustainable practices, environmental ethics, and conservation efforts into the tourism industry, ensuring that development does not compromise the well-being of future generations or the natural ecosystems upon which they depend. Nagata and Shirayama put forward that in 2020 that the effect of knowledge diffusion on social and economic development and its scale economy have also attracted great attention from scholars at home and abroad [7]. Su et al. [8] highlighted several key attributes of knowledge: its non-excludability, the phenomenon of increasing marginal returns, and the fact that its transfer incurs certain costs and may result in intangible losses. Rosalba put forward that the establishment and interaction of knowledge networks of university research institutions, companies, governments and other organizations contribute to economic growth in 2003 [9]. Ghio et al. discussed the scale economy of knowledge innovation, put forward the concept of endogenous and spillover of knowledge innovation [10]. They established a model to analysis the market basis of knowledge innovation spillover and the optimal efficiency of knowledge innovation spillover and the scale of knowledge diffusion. In 2019, Cheng and Shiu studied the knowledge diffusion structure in the economy and society through the network structure [11]. Cheng considered the dynamic change of the knowledge diffusion over time. And he also discussed the source, size and characteristics of scale economy of knowledge diffusion. In 2020, Marques et al. demonstrated that analyzing the influencing factors of knowledge diffusion forms the basis of knowledge diffusion modeling [12]. They believed that institutional factors, knowledge confidentiality, distance, policy, capital and imbalance of ideas determine the effect of knowledge diffusion in a certain degree. The knowledge of ecological civilization is inherently non-exclusive. The more ecological civilization knowledge the public understand, the more obvious it will be for environmental protection and restoration. It is also in line with the characteristics of increasing marginal return of knowledge. Addressing the relationship of knowledge diffusion networks in other areas, we find that these networks often serve as a robust basis for interpreting the dynamics in our study. For instance, the establishment and interaction of knowledge networks among universities, companies, and governments contribute significantly to economic growth and innovation. This perspective is crucial for understanding how visitor education and knowledge diffusion in tourism can lead to sustainable development and ecological conservation. The working paper by Baggio, Cooper, McLeod and Scott in 2008 describes basic definitions and computational techniques, examines the effects of network topology on dynamic properties, and illustrates these concepts using a tourism destination, elba, italy, as a case study. So it is theoretically feasible to establish a knowledge diffusion network model to further study the Tourism Destination-Public ecological civilization knowledge diffusion path, diffusion quantity, diffusion cost, social and economic value.

Basic assumptions of knowledge diffusion network in scenic spots

The knowledge diffusion network refers to the interconnected system through which knowledge and information are shared, transferred, and spread among individuals, organizations, or communities. This network typically involves various channels, such as interpersonal communication, formal training programs, informal exchanges, and digital platforms. The network includes both formal structures, like organizational hierarchies and communication protocols, as well as informal relationships and communities of practice. The working paper by Baggio, Cooper analysis static structural characterization of the network and a dynamic analysis of the information diffusion process, with discussion of the outcomes and implications for destination management. Developed and inspired by Baggio, the knowledge diffusion network in scenic spots can be defined as G_c(t) = (V_C,E_C). V_C is represented as a set of given core nodes (scenic spot network), that is, the set of network members. E_C is the edge of the connection tree between network nodes. N_c = |V_C| represents the total number of nodes in the core nodes set in time t. A_t = (a_ij)_t represents the connection matrix in time t. If the scenic spot node i and j form a connection tree, then a_ij = 1, otherwise a_ij = 0. The degree of node i refers to the number of connection trees which are directly connected to the node. The degree of node i can be calculated by . The degree of node reflects the ability of individual node to spread knowledge outward, and also reflects the node’s intensity of possession of network resources.

As the number of visitors and the virtual tourist groups of scenic spots grows at all times, the number of people in contact with those tourists after returning to their places of residence with newly accepted knowledge also grows. The reception, digestion and dissemination of knowledge of different people is different. And the distribution of interpersonal communication is uneven. The state of connectivity in the network and the degree of nodes are different. Therefore, the knowledge dissemination of scenic spots-visitors-public has two characteristics: the node growth of scale-free network and the preferred connection. First, the diffusion of tourism knowledge is not carried out on all network nodes at the beginning. It begins to spread from some nodes firstly, such as from the 5A-level scenic spots to the surrounding areas of the scenic spots, and then gradually spreads to other scenic spots throughout the country. Second, the spread of tourism knowledge is often started by well-known scenic spots. Because well-known scenic spots undoubtedly have richer network resources. Theirs ecological civilization management experience is also richer because the state invested more related resources. The exemplary effect of famous scenic spots is especially obvious. Therefore, famous scenic spots have more opportunities to other scenic spots comparing with general scenic spots. Their degree will be particularly rich. In the other words, the distribution of scenic spots’ degree is not uniform and has the characteristics of power law distribution. The connecting trees between nodes are gradually formed. The connecting trees between scenic spots are often formed from part of the local scenic spots, then spread to the nodes of the whole network.

As the number of visitors and virtual tourist groups to scenic spots continues to grow, so does the number of people who engage with these tourists upon their return to their places of residence, armed with newly acquired knowledge. However, the reception, digestion, and dissemination of this knowledge varies among different individuals. Interpersonal communication is also unevenly distributed, with varying levels of connectivity within the network and different degrees of nodes. Consequently, the knowledge dissemination process involving scenic spots, visitors, and the public exhibits two key characteristics: the growth of scale-free network nodes and preferred connections.

Firstly, the diffusion of tourism knowledge does not occur simultaneously across all network nodes at the outset. Instead, it begins spreading from select nodes, such as 5A-level scenic spots, to the surrounding areas before gradually extending to other scenic spots throughout the country. Secondly, the spread of tourism knowledge often originates from well-known scenic spots due to their richer network resources and ecological civilization management experience. These famous landmarks have a more significant impact on other scenic spots compared to ordinary ones because they possess more extensive networks and state-backed resources. The exemplary effect of renowned scenic spots is particularly pronounced, leading them to have more opportunities to connect with other attractions than lesser-known ones. As a result, the distribution of scenic spots’ degrees is non-uniform and follows power law distribution patterns. Connecting trees between nodes gradually form, with local scenic spots often serving as the starting point for establishing links between various attractions before expanding to encompass the entire network.

Topology of knowledge dissemination network for nodes in scenic spots

Through the aforementioned analysis, it becomes evident that the dissemination of knowledge within scenic spot nodes exhibits characteristics of a typical scale-free network. Therefore, a scale-free network model (BA model) is employed to depict the topology of the knowledge dissemination network in scenic spots. The Barabási-Albert (BA) model is a popular model for generating scale-free networks. It’s based on the idea that new nodes in a network preferentially attach to already well-connected nodes, leading to a few nodes with many connections and many nodes with only a few connections. To use the BA model to depict the topology of the knowledge dissemination network in scenic spots following the 7 steps including:

1. Step1.initialization: Start with a small number of nodes and edges.
2. Step 2. Adding New Nodes: At each step, add a new node to the network. This new node will connect to a randomly chosen existing node.
3. Step 3. Weighted Edges: Instead of connecting the new node to any old node, you can introduce a weighted mechanism.
4. Step 4. Repeat: Steps 2 and 3 are repeated for a certain number of iterations or until the desired number of nodes is reached.
5. Step 5. Scaling: To ensure that the resulting network has a power-law degree distribution.
6. Step 6. Visualization: Once you have generated your scale-free network using the BA model, you can visualize it using various tools and techniques. Nodes with many connections (hubs) will stand out, while others will be less connected.
7. Step 7. Comparison with Empirical Data: After generating the network, you can compare its properties (like degree distribution, clustering coefficient, etc.) with empirical data from real-world scenic spot knowledge dissemination networks. This will give you insights into how well the BA model captures the characteristics of the real-world network.

At present, the methods of studying node degree distribution are as follows: mean field method [13, 14], rate-equation method [15], master equation method [16] and numerical calculation method based on Markov chain [17, 18]. The author uses the mean field method to study the distribution of scenic spots degree in knowledge dissemination network in this paper. Given that the scale-free network model differs from the actual situation of the knowledge dissemination network in the scenic area, the following algorithm is utilized to create a scale-free network, illustrated in Fig 1.

(1) Linear growth of nodes in scenic spots: the network of scenic spots starts with a small number of nodes N_c0, adding a new node with m(m<N_c0) edges within a certain time, and connecting the new nodes to the old nodes.

(2) preferential attachment: The probability that the new node is connected to the old node i depends on the degree of the node i and the attraction level of the scenic spot itself. Attraction level of the scenic spot is defined as w_i. w_i is the abilities of the node attracting to other nodes, such as the ecological environment of the scenic spot, history and culture, management concept, tourist reputation, etc. Suppose the scenic spot has n attributions, it can be simply represented as . The preference probability of the new node selecting the old node to connect is: (1)

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Fig 1. Scale-free network for knowledge dissemination in scenic spots.

https://doi.org/10.1371/journal.pone.0310112.g001

In scenic area knowledge dissemination network, famous attractions often have a higher number of connections, which means they are easier to be visited and visited by tourists. Here are some of the reasons why the famous attractions are of a high level:

Popularity: Famous scenic spots often have high visibility, attracting a large number of tourists to visit. These attractions are usually publicized through various channels, such as media reports, travel guides, social media, etc., so that more tourists can know and choose to visit.

Convenient transportation: Many famous scenic spots are usually located in convenient places, such as city centers, railway stations, and near airports, which make it easier for tourists to reach these attractions. In addition, these attractions usually have good public transportation facilities, such as subway, bus, taxi, etc., to facilitate tourists to go to other scenic spots during the tour.

Rich tourism resources: Famous scenic spots usually have rich tourism resources, such as natural landscape, historical sites, culture and art, etc., which attract a large number of tourists to visit. At the same time, these scenic spots usually also provide a variety of tourism services, such as tour guides, accommodation, catering, etc., to further facilitate the tourists to visit.

Social network effects: The height of famous scenic spots is also influenced by social network effects. When more and more tourists choose to visit a famous scenic spot, the popularity and attraction of this scenic spot will be further increased, thus attracting more tourists. In addition, word-of-mouth among tourists will encourage more people to choose to visit these famous attractions.

Tourism policy and planning: The support and planning of the government and relevant departments will also affect the degree of famous scenic spots. For example, the government may invest in the infrastructure of tourist attractions to improve the accessibility and comfort of scenic spots, or formulate preferential policies to attract tourists to visit.

In short, the famous scenic spots have a high degree, which is the result of a combination of many factors. These famous scenic spots not only have high visibility and attraction, but also have good tourism resources and services, as well as convenient transportation conditions. Therefore, in the tourism network, these famous attractions are often the main destinations for tourists.

In the following section, it is an interesting way to demonstrate that the knowledge dissemination network of a scenic area can be theoretically considered as a scale-free network with power law distribution. To analyze the node distribution, it is useful to employ the mean-field method for calculation. The degree of the node in the network can be assumed to be d_i. d_i is a continuous real value variable. According to the above two algorithms, the change rate of d_i is proportional to the probability p_i. Thus the following differential equations are satisfied: (2) Taking into account m new nodes connect to an old node with degree d_i at a certain time interval, the increase degree of the network in time t is A = m. m can be obtained by using the following equation: (3) where , w_i obey a normal distribution with the mean value μ and the variance σ². when t is sufficiently large, the following formula can be obtained. (4) Substitute Eq (3) to Eq (4), the following equation can be derived. (5) By solving differential Eq (5), the solution can be obtained. (6) The initial condition of differential equation Eq (5) is as follows: d_i(t_i) = m.The solution of Eq (5) can be obtained as (7) From Eq (7), it is evident that (8) Assuming the time interval of adding a new node is equal, then t_i is uniformly distributed (9) When Eq (9) is substituted into Eq (8), the following can be obtained (10) The distribution of node’s degree in the constructed network by above algorithm can be calculated as follows (11) The node degree distribution of scenic network is power law distribution with γ = 3+μ/m. According to the empirical research, the power index of network nodes in scenic spots is power law distribution with γ = 3, which is consistent with the theoretical model: (12) For example, Eq (3) shows that there are N_c0 = 50 nodes initially, when adding a node in a certain time interval, then it can generate a scale-free network with 300 nodes show as following Fig 1.

The simulation in Fig 1 shows that as time increases, more non-core nodes are generated. This is because each new node generates a link to an existing node in the network, which can eventually form a scale-free network for Knowledge Dissemination in Scenic spots. The network’s structure is based on the Barabási-Albert (BA) model, where each new node has power law distribution with γ = 3 of being connected to the existing nodes in the network.

Basic characteristics of scale-free network for knowledge diffusion in scenic spots

The clustering coefficient and the average path length reflect the two basic features of the network topology. The clustering coefficient reflects the degree of network node collectivization. In the social network, the group represents the circle of friends or acquaintances in the network. The members of the group are often familiar with each other and have a high degree of stability [19, 20]. There are also collectivization characteristics in the knowledge diffusion network of scenic spots. There are many close links in the same administrative region (the same province or city), so the clustering coefficient is also utilized to reflect the collectivization characteristics of the knowledge diffusion network of scenic spots. Clustering coefficient has different definitions [21]. The commonly used definitions are as follows: for a scenic spot node i, the set of nodes of directly connected scenic spots i is V_i,then n_i = |V_i|.The cluster coefficient of the scenic spot node can be defined as: (13) For all nodes in the scenic spots set, the average clustering coefficient is: (14) Network average path length represents how many steps a node takes on average to connect to another node, which reflects the effectiveness of communication between network nodes. sl_ij is the shortest path between scenic spot nodes i and nodes j, then the average path length of scenic spot network L_c is (15) Robin Cowman think that the shorter network path length means faster knowledge diffusion, and the higher clustering coefficient can make the knowledge diffusion between the sub-groups of the network spread rapidly, but the exclusion of the sub-groups hinders the knowledge diffusion between the groups.

Results

Under the above scale-free network topology, the dynamics of ecological knowledge dissemination network in scenic spots are further explored. The node of scenic spot can acquire ecological knowledge, historical and cultural knowledge and some management experience of how to spread knowledge through its own research, development and observation; on the other hand, the scenic spot can also acquire knowledge from other nodes by connecting trees. Wang et al. pointed out in 2015 that knowledge diffusion is affected by many factors, such as the learning ability of knowledge recipients, the reputation of knowledge communicators, and the communication situation between people [22]. The efficiency of knowledge diffusion tree between scenic spots is also affected by these similar factors. It is believed that the knowledge acquired by nodes in the connection tree is related to the knowledge content of the connected nodes, and is also influenced by the knowledge diffusion efficiency of the connection tree, as well as the number of connection trees.

After applying the proposed methods, we obtained several key results. Firstly, we observed that the knowledge content of each node increased over time, influenced by both the new knowledge generated internally and the knowledge acquired from connected nodes. Secondly, we found that nodes with a higher number of connection trees had a significantly greater capacity for knowledge diffusion, demonstrating the importance of establishing multiple connections within the network. Thirdly, the efficiency of knowledge diffusion varied across different connection trees, highlighting the role of communication channels and the learning abilities of the nodes involved.

Essentially, the knowledge contained in each node is different, so it can be reasonably assumed that each node in the core nodes set has varying abilities to spread knowledge. And the connection tree structure between nodes is different, so the efficiency of diffusion knowledge is also different. The total number of nodes in the core nodes set is N_c and the knowledge content of the node i in the core nodes set is K_{cnode_i}.According to the above assumptions, the knowledge content of the node K_{cnode_i}(t) in time t should include the new knowledge S_{cnode_i}(t) created by the node itself and the knowledge obtained by the connection tree from other nodes K_{ctree_i}(t). K_{cnode_i}(t) can be expressed by the following formula: (16) where , . And n(K_{ctree_i}) represent number of connection trees of node i at time t, then and . It can be seen that the total knowledge storage of the whole core nodes set K_cnode at time t is: (17) Property 1: the knowledge content K_{cnode_i}(t) of the core node i should increase with the new knowledge generated by the node itself, that is , with the increase of the knowledge obtained from the connection tree, that is , with the increase of the number of connection trees of node i, that is .

K_{cnode_i}(t) can be represented by the following simple model by Property 1, (18) From the previous hypothesis, it becomes evident that the structure of the connection tree between the two nodes varies, aligning more accurately with the actual scenario. The communication channels established by the two scenic spots are different, and the efficiency of communication is also different. The amount of knowledge of nodes i obtained by the connection tree between nodes i and j at time t is decided by the width of the communication path of the connection tree w_cij, the flow rate of the communication path v_cij, the knowledge content on the left side of the communication path K_{cnode_i}, and the knowledge content on the right side of the communication path K_{cnode_j}. Hence K_{ctree_ij}(t) can be expressed as follows: (19) It is assumed that the width of the communication path does not change with time in lifetime of the connection tree. The flow rate increases with time in the initial stage of the connection tree, and the flow rate of the communication path decreases until it drops to 0 after some time of the connection tree begin, that is and . In addition, the knowledge content difference between the left and right sides of the communication path , if ΔK_{cnode_ij}≤0, then the flow rate of the communication path decreases to 0. The following indication function can be expressed (20) Eq (19) can be further expressed as follows (21) where b_cij represents the absorption coefficient of the node i in the process of knowledge diffusion from the node j to the node i. It represents the ability of the node i learning new knowledge. By substituting Eq (21) into Eq (18), the following can be derived: (22) The knowledge storage K_cnode the whole core nodes set in Eq (17) can be further expressed as (23)