Is One-Way Multi-Station Feasible? Influence of Value and Cost on Travel Intention of Urban Agglomeration in the Guangdong–Hong Kong–Macao Greater Bay Area

Abstract

Regional tourism with urban agglomeration as the spatial carrier has become one of the flow characteristics of many tourists. Tourists visiting multiple cities at one time will certainly gain more value than visiting one city, but they will also pay more costs, thus affecting their emotions and willingness to travel. But the mechanism and theoretical boundaries have not been clarified. Based on the cognitive–affective–behavioral attitude model and the customer-delivered-value model, this paper takes the tourism destination tourists of the Guangdong–Hong Kong–Macao Greater Bay Area urban agglomeration as samples to explore the influence mechanism of tourists’ perceived functional value, social value and knowledge value on positive emotion and travel intention, and to test the moderating mediating effect of monetary cost, energy cost and psychological cost on positive emotion. Social value and knowledge value positively affect travel intention. Functional value, knowledge value and social value positively affect positive emotion, and positive emotion positively affect travel intention. Positive emotion plays a complete mediating role between functional value, social value and travel intention, and plays a partial mediating role between knowledge value and travel intention. Monetary cost, energy cost and psychological cost respectively moderate the mediating effect of positive emotion between functional value and travel intention. Psychological cost moderates the mediating effect of positive emotion between social value and travel intention. Monetary cost and psychological cost respectively moderate the mediating effect of positive emotion between knowledge value and travel intention. Differences in the impact of consumers’ perceived value at different levels on positive emotion and travel intentions. It opens up the differences in the effects of the interaction of different levels of perceived value and different types of perceived cost on positive emotion. From an empirical point of view, it is proved that the tourism boutique line design based on multicity destination combination in China is feasible as a method to promote tourism integration and regional tourism brand building. It also puts forward the management enlightenment of regional tourism integration and regional tourism brand building from the perspectives of enterprises, front-line service personnel, residents and government.

1. Introduction

With the improvement of people’s economic level and the improvement of transportation infrastructure between cities, the behavior of tourists has changed dramatically. The demand for individualized and diversified experiences has gradually dominated the tourism market [1]. In their leisure time, an increasing number of travelers use self-driving as a way to travel to multiple cities at once for travel experiences according to different travel routes [2].

This has led to a shift in tourism activity and consumption space from a single city to an entire regional urban agglomeration [3], helping tourists to better understand the social context and cultural characteristics of the city they visit and the region to which it belongs [4]. However, what factors lead to the willingness of tourists to visit more than one city at a time, as opposed to visiting only one city at a time? In the supply perspective, it has been pointed out that the purpose of cross-regional tourism itinerary design is to maximize tourists’ entertainment [5], which not only enhances the tourists’ experience but also improves the competitive advantage of the destination [6,7]. From the demand perspective, it has been noted that tourists are eager to search for other cultures, seek adventure in different environments, learn about the cultures of different regions, and meet more diverse people by searching for unusual routes [8]. This actually reflects that traveling to multiple cities brings more perceived value to the tourist, because the perception of value includes not only the quality of tourism services and functional benefits, but also the social benefits resulting from achieving the tourist’s communicative purpose and social interaction, and even more intellectual connotations and values [9,10,11]. Therefore, this paper argues that different levels of perceived value will influence the willingness of tourists to visit multiple cities at once.

Furthermore, as the research on the role of emotion in the cognitive–behavioral processes of tourists continues to intensify, more and more destination managers are demanding efforts to enhance the positive emotions of tourists [12], thus better motivating their willingness to travel. Therefore, it is important to pay attention to the role of emotions in the transmission between perceived value and travel intention. The cognitive–affective–behavioral attitude model of consumer behavior research suggests that consumers generate certain perceptions through products or services, which lead to positive or negative emotions in their psychology and thus influence their behavioral intentions [13]. Similarly, the process of tourism activity not only includes the identification and perception of value, but also the resulting emotional changes, which affect the traveler’s behavioral intentions. For example, Morris et al. explored the mediating role of affective attitudes between cognitive and intentional attitudes and demonstrated that emotions have a stronger explanatory power than cognition for intentional attitudes [14]. In summary, travel emotions may be an intrinsic mechanism to explain the influence of travelers’ perceived value on travel intentions.

However, the theory of customer-delivered value points out that consumers will consider the difference between value and cost when making behavioral decisions [15]. That is to say, consumers often choose products or services with the highest value and the lowest cost, that is, products or services with the largest customer-delivered value as the priority consumption object. Therefore, when tourists consider going to multiple cities, it is necessary to search and understand the relevant information of these destinations in order to respond to different situations that may arise during the trip [16]. For example, itinerary planning, hotel reservations, understanding of urban customs and culture, and anticipation of unexpected events such as terrorism, public health, natural disasters, social security [17] and other issues that require random response to difficulties. It can be seen that tourists need to spend a lot of monetary, energy and psychological costs in order to better ensure the smooth and safe journey. Therefore, tourists’ perception of different levels of value is bound to be interfered by different types of perceived cost, which has a certain degree of influence on their emotion and tourism intention. Therefore, this paper argues that on the basis of exploring the impact of perceived value on emotion and travel intention, we should also pay attention to the moderating effect of perceived cost, which makes it important to explore the interaction between perceived value and perceived cost and its subsequent impact.

As one of the four largest bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area is promoting the in-depth cooperation of cities within the urban agglomeration by promoting the intermodal transportation of aviation, highway and high-speed rail, forming a ‘one-way and multiple stations’ excellent tourism route, creating a regional tourism brand with world influence, and providing tourists with multi-destination and multi-category travel experiences. Nowadays, urban tourism destinations such as Guangzhou, Shenzhen, Hong Kong, Macao and Zhuhai are constantly favored by tourists. Tourists visit these cities through group tours or self-driving tours, which further promotes the process of regional tourism integration in the Guangdong–Hong Kong–Macao Greater Bay Area. Therefore, based on the cognitive–affective–behavioral attitude model and customer-delivered-value theory, this paper discusses the influence mechanism of perceived value and perceived cost on the travel intentions of urban agglomeration in the Guangdong–Hong Kong–Macao Greater Bay Area. The main solution is to reveal the differences in the impact of perceived value at different levels, such as functional value, social value and knowledge value, on travel intention, and to verify whether tourists have different behavioral attitudes towards the choice of tourism destinations in urban agglomerations due to different levels of value, so as to better create different levels of value for the tourism routes of multicity combinations. Secondly, the cognitive–affective–behavioral attitude model is used to analyze the transmission mechanism of different levels of perceived value on travel intention through positive emotions, so as to refine the types of different intermediary roles, identify the key role of positive emotions in the cognitive process, and better find the focus to stimulate tourists’ positive emotions. Third, according to the theory of customer-delivered value, this paper analyzes the moderating effect of perceived cost between perceived value and positive emotion, and reveals this under different critical conditions.

2. Core Concepts and Theoretical Basis

2.1. Core Concepts

2.1.1. Trave Intention

Travel intention is the intention of tourists to travel to a certain destination [18], including tour ideas, revisit ideas and willingness to recommend [19]. It is the most accurate behavior attitude to predict tourists’ consumption behavior [20]. In addition to macro factors such as social environment and economic environment [21], micro factors such as individual attitude have become important influencing factors for academic circles to explore travel intention [22]. Among them, the impact of individual cognitive attitudes such as perceived value and perceived cost on travel intention has been studied more [23]. With the advent of the experience economy, emotional attitudes are increasingly affecting travel intention [24]. However, related studies have not focused on tourists’ intention to travel to visit urban agglomerations with multiple cities as combinations. Therefore, this paper focuses on the effects of perceived value, perceived cost and positive emotion on tourists’ intention to travel to urban agglomerations, which helps to reveal new features of the mechanisms influencing travel intention to visit multiple cities at once.

2.1.2. Perceived Value

In tourism research, scholars tend to define perceived value from both a utilitarian “trade-off between gains and losses” [25] and a purely “gains” perspective [26]. In terms of trade-offs between gains and losses, Zeithaml considers perceived value as the customer’s overall evaluation of the utility of a product based on a trade-off between costs and gains [27]. More scholars measure both gains and costs components together, or evaluate the net value of gains and costs [28]. In fact, the composite measure cannot explain the magnitude or interaction between “gains” and “losses”, and the net value of “gains” and “losses” is difficult to measure precisely through numbers. As Holbrook argues for other types of value than functional value, it is often difficult to obtain an evaluation of value through calculations based on consideration of the effort corresponding to the benefit obtained [29]. Thus, this paper endorses the analysis of the perceived value of tourism from a “gains” perspective. The “gain” perspective of value perception refers primarily to derived value, defined as the benefits derived from the experience associated with consumption, and this value is independent of any feelings of loss, focusing on the outcome of the consumer experience [30]. In other words, the derived value does not include consideration of the payoff aspect, but only the benefit.

In terms of the dimension division of perceived value, Gronroos believed that it should be divided into cognitive value and emotional value [31]. Sweeney et al. believed that it should be divided into functional value, emotional value and social value [32]. Sheth et al. claimed that it should be divided into social value, emotional value, functional value, knowledge value and situational value [33]. Xu et al. deemed that it should be divided into functional value, social value and knowledge value [34]. From Maslow’s hierarchy of needs theory, consumers should first meet the lower level of needs, the most basic individual requirements for products or services; these are often functional [35]. Functional value is a self-oriented external value, emphasizing the practical experience of the object [36]. After the functional value is satisfied, consumers often seek to improve their status and social respect, which is manifested as social value, that is, consumers gain social benefits in the use of products or services [32]. This lets them feel that by consuming a product or service, they can be admired, respected and complimented by others, helping them to gain social identity and enhance their self-image. A higher level of demand is the demand for self-realization, that is, people pursue their ability or potential to obtain more knowledge, understanding, curiosity, exploration, meaning and predictability. This is the value of knowledge; that is, the perceived utility of curiosity, freshness and thirst for knowledge caused by consumers’ consumption choices [11]. Therefore, this paper attempts to explore the influence of perceived value on travel intentions from these three dimensions.

2.1.3. Positive Emotions

Emotion is the attitude experience of tourists to whether the objects meet their needs [37]. In terms of the influencing factors of tourists’ emotions, the research mainly focuses on the influence of individual cognitive evaluation of certain emotions or overall emotions [38]. From the perspective of consumption, scholars differ in the division of emotion, but basically summarize that consumption emotion is split between two independent concepts of PE and negative emotion [39]. Moreover, positive emotion is often used as a mediating variable to connect the cognitive and behavioral elements of travelers [40]. Therefore, this paper uses positive emotion as a mediating variable to explore the mechanism of the influence of travelers’ perceived value on travel intention.

2.1.4. Perceived Cost

The perceived cost is defined as the cost of obtaining a utility [27]. In the dimension of perceived cost, it is generally discussed from the perspective of monetary cost and nonmonetary cost [41]. In addition to monetary cost represented by money expenditure, nonmonetary cost mainly includes energy cost and psychological cost. Energy cost includes time cost, mental cost and physical cost [42]. Psychological cost refers to the psychological ‘unhappiness’ that customers feel in the process of consumption, including risk cost, switching cost and situational cost [43]. It refers to the cost loss caused by decision-making errors, product replacement or resale, and unpredictable environmental factors. On the whole, the three dimensions, including monetary cost, energy cost and psychological cost, are actually the process from physical feeling to psychological feeling, and from concrete to abstract. Therefore, we believe that the dimensions of perceived cost can be summarized as monetary cost, energy cost and psychological cost. This paper attempts to explore the role of boundaries in the perceived value of tourism on the mechanism of positive effects from these three dimensions in order to examine the role of mediating effects of positive emotions under different boundary moderations.

2.2. Theoretical Basis

2.2.1. Cognitive–Affective–Behavioral Attitude Model

Consumer perception studies tend to analyze cognitive factors in the perception process [44], often at the expense of emotional factors [45], without fully recognizing the interaction between cognitive and emotional dimensions [46]. The cognitive–affective–behavioral attitude model was proposed by Hoveland [47]. It emphasizes the interrelationship between the cognitive, affective and behavioral dimensions of mental processes to reflect consumer attitudes. It is generally explained in terms of three hierarchical effects (standard learning, low-intervention and experiential hierarchies) [48]. The standard-learning hierarchy assumes that individuals will widely collect information before making decisions to establish their own in-depth understanding of each option. On this basis, individuals form their own feelings of choice and then produce behavioral intentions of choice, such as travel intention [49]. For this paper, travelers are bound to spend a lot of time and effort in searching for information and planning their itinerary in advance for a one-time trip to multiple cities, and they also make contingency plans for unexpected events. This highly involved characteristic is bound to directly cause changes in their own emotions and thus have an impact on their willingness to travel. Therefore, this study selects standard learning hierarchy types, and explores the relationship between travelers’ perceived value, positively emotional responses and travel intention to examine the driving mechanisms that influence travel intention in urban agglomerations.

2.2.2. Customer-Delivered Value Theory

Customer-delivered value refers to the difference between the total value of customer purchase and the total cost [15]. Total customer value is the expected benefit when purchasing a product or service, and total customer cost is the cost of purchasing a certain product or service. Customers are value-maximization seekers. Therefore, destinations and businesses should provide higher delivered value to increase customer satisfaction and loyalty [50]. This theory is more often applied to the automotive industry [51], banking industry [52] and hotel industry [53], but has not been applied to urban agglomeration tourism contexts. For this article, rational travelers are able to determine that traveling to multiple cities will provide the highest value and make choices that are beneficial to them. Subject to certain monetary, energy and psychological costs, customers are value maximizers who form a value expectation and respond to it by acting on it. They then learn whether the tour meets their desired value, which will influence their willingness to travel. This shows that the influence of perceived value will be interfered by perceived cost, thus having a certain degree of interaction effect on consumption emotion and behavioral intention. Based on this theory, this paper explores the moderating role of perceived cost as a moderating variable between perceived value and positive emotion, and explores the boundary regulation where positive emotion exerts a mediating effect.

3. Research Hypothesis

3.1. The Main Effect of Perceived Value and Travel Intention

Travel intention is the most accurate behavior attitude to predict tourists’ consumption behavior [54]. Consumer behavior scholars believes that consumers will search and evaluate some products and sales channel information and try to make satisfactory decisions [55]. Therefore, studies have proved the positive relationship between perceived value and travel intention. Tourists with high perceived value for three South American destinations, such as Bianchi, Pike and Lings [56], are more likely to travel there. However, most of the previous studies start from the comprehensive perspective of ‘gain and loss’, and explore the positive effect of consumer value on travel intention through the utility between acquisition and payment. This paper argues that tourists’ perceived value will be higher after the consideration of coping with the tourism of urban agglomeration represented by multicity destinations is removed, so they have a stronger willingness to travel.

Most scholars believe that consumers should first meet the lower level of needs, i.e., the most basic individual requirements for products or services; these are often functional [9]. Functional value is a self-oriented external value, emphasizing the practical experience of the object [36]. Tourism in urban agglomerations is manifested in tourists’ pursuit of the quantity, quality and cost performance of urban destinations. Secondly, after the functional value is satisfied, consumers often seek to improve their status and social respect, which is manifested as social value, that is, consumers gain social benefits in the use of products or services [32]. This allows them to feel that consuming a product or service will enable them to be admired, respected and complimented by others, helping them to gain social identity and enhance their self-image. In urban agglomeration tourism, tourists obtain respect, envy and praise from others by visiting multiple urban destinations, thereby increasing their social identity and enhancing their self-image. A higher level of demand is the demand for self-realization, that is, people pursue their ability or potential to obtain more knowledge, understanding, curiosity, exploration, meaning and predictability. This is the knowledge value, that is, the perceived utility of curiosity, freshness and thirst for knowledge caused by consumers’ consumption choices [11]. In urban agglomeration tourism, tourists can obtain more knowledge, understand more cultural phenomena, satisfy their curiosity and cultivate the spirit of exploration and prediction ability by visiting multiple urban destinations. Therefore, this paper believes that functional value, social value and knowledge value represent perceived value at different levels, and they all have a positive impact on travel intention. Thus, this paper puts forward the following assumptions:

H1: 

The perceived value of tourists visiting multiple city destinations in a row has a positive impact on travel intention.

H1a: 

The perceived functional value of tourists visiting multiple city destinations in a row has a positive impact on travel intention.

H1b: 

The perceived social value of tourists visiting multiple city destinations in a row has a positive impact on travel intention.

H1c: 

The perceived knowledge value of tourists visiting multiple city destinations in a row has a positive impact on travel intention.

3.2. The Mediating Effect of Positive Emotion

In terms of the influencing factors of tourism emotion, the research mainly focuses on the influence of individual cognitive evaluation on certain emotions or overall emotions [38]. From the perspective of consumption, scholars differ in the division of emotion, but basically summarize consumption emotion as two independent concepts of positive emotion and negative emotion [39]. Studies have shown that tourists’ cognitive factors have a significant impact on consumer sentiment. For example, Walsh et al. found that service cognitive factors have a significant impact on customer consumption emotion [57]. In other words, when tourists perceive stronger perceived value, they will have stronger positive emotion. Therefore, when tourists perceive the value of urban agglomeration tourism characterized by multicity destinations, they realize that continuous visits to multiple cities will produce strong practical experience. For example, when tourists come to the Guangdong–Hong Kong–Macao Greater Bay Area for tourism, they will often cross the customs to Hong Kong and Macao after visiting Shenzhen, resulting in positive emotion. Some tourists will think that continuous tour of multiple cities can provide them with the opportunity to make friends, cause envy in others and obtain social recognition, so as to produce positive emotion. Some tourists will think that a continuous tour of multiple cities will allow them to obtain more information about the region’s tourism, understand the characteristics of various urban cultures, and thus generate positive emotion. Based on the above discussion, the following research hypotheses are proposed:

H2: 

The perceived value of tourists visiting multiple city destinations in a row has a positive effect on positive emotion.

H2a: 

The perceived functional value of tourists visiting multiple city destinations in a row has a positive effect on positive emotion.

H2b: 

The perceived social value of tourists visiting multiple city destinations in a row has a positive effect on positive emotion.

H2c: 

The perceived knowledge value of tourists visiting multiple city destinations in a row has a positive effect on positive emotion.

The study of the effects of tourism emotions focuses on the relationship between tourists’ emotions and satisfaction or behavioral intention. White et al. points out that an important factor to affect tourists’ behavior is emotion [58]. Nyer shows that tourists’ consumption emotion has a significantly positive impact on their behavioral intentions such as oral publicity and revisit tendency [59]. Positive emotion can be described respectively by adjectives represented by happiness and excitement [60]. Many studies have confirmed that tourists’ positive emotion on the image of a tourism city have a significantly positive impact on travel intention [61,62]. Similarly, for continuous visits to multiple urban destinations, if tourists have a positive emotion, it means that this positive emotion can drive them to form a strong willingness to travel. Therefore, the following research hypotheses are proposed:

H3: 

The positive emotion of tourists visiting multiple city destinations in a row has a positive effect on travel intention.

The above discussion actually proves the views of previous researchers that all human behaviors are a combination of spiritual, emotional and physical dimensions [63]. This makes the cognitive–affective–behavioral attitude model become an important model to explore the antecedents and outcome variables of tourism emotion. Generally, it is explained by the classification of three hierarchical effects, namely, standard learning, low intervention and experience level. Among them, the standard-learning hierarchy has been widely introduced into the study of tourism consumption behavior [64], which provides theoretical support for revealing the mediating role of tourism emotion in the mechanism. For example, Mitas et al. verifies the mediating role of positive emotion between social relations perceived by mature tourists and revisit intention [65]. Yǚksel confirmed that shopping environment had an impact on revisit intention and other behaviors through the mediating effect of consumer emotion [38]. Morris et al. found that emotional attitude played a mediating role, and at the same time, it had a stronger explanatory power than cognition on intentional attitudes [14]. Similarly, if tourists perceive the functional value, social value and knowledge value of tourism destinations in urban agglomerations, they will have a certain positive emotion, which further promotes their willingness to continuously visit multiple tourism destinations. Therefore, the following research hypotheses are proposed:

H4: 

The positive emotion generated by tourists visiting multiple urban destinations in a row play a mediating role between perceived value and travel intention.

H4a: 

The positive emotion generated by tourists visiting multiple urban destinations in a row play a mediating role between perceived functional value and travel intention.

H4b: 

The positive emotion generated by tourists visiting multiple urban destinations in a row play a mediating role between perceived social value and travel intention.

H4c: 

The positive emotion generated by tourists visiting multiple urban destinations in a row play a mediating role between perceived knowledge value and travel intention.

3.3. The Moderating Mediating Effect of Perceived Cost

Customer-delivered value refers to the difference between the total value of customer purchase and the total cost [15]. This shows that the influence of perceived value will be interfered by perceived cost, thus having a certain degree of interaction effect on consumption emotion and behavioral intention. Therefore, this paper puts forth that perceived cost will affect the mediating effect of positive emotion on perceived value and travel intention. Further extended to urban agglomeration tourism, with the expansion of the scope of the tour space, it will greatly improve the perceived value of tourists at different levels; however, compared to single-destination travel, multi-destination travel inevitably causes the traveler to have higher monetary costs represented by money, greater physical, mental and energy costs represented by time and to encounter unpredictable environmental factors such as climatic and meteorological conditions [66], public health events [67], social security issues [68] and terrorism in the during the trip [69]. This leads to the early termination of the planned itinerary, making it difficult for the tourist to choose an alternative tourist area in time and to bear more losses due to their decision making, making the tourist pay greater psychological costs. Therefore, these foreseeable and unknown perceived costs would strongly disrupt the mechanism of perceived value’s influence on positive emotions and travel intentions. The interaction between different levels of perceived value and different types of perceived cost will inevitably further influence the mediating role of positive affect. The following research hypothesis is therefore proposed:

H5: 

The perceived cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived value and travel intention.

H5a: 

The perceived monetary cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived functional value and travel intention.

H5b: 

The perceived monetary cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived social value and travel intention.

H5c: 

The perceived monetary cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived knowledge value and travel intention.

H5d: 

The perceived energy cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived functional value and travel intention.

H5e: 

The perceived energy cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived social value and travel intention.

H5f: 

The perceived energy cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived knowledge value and travel intention.

H5h: 

The perceived psychological cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived functional value and travel intention.

H5i: 

The perceived psychological cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived social value and travel intention.

H5j: 

The perceived psychological cost of visiting multiple cities in a row for tourists moderates the mediating role of positive emotion between perceived knowledge value and travel intention.

4. Materials and Methods

4.1. Measures and Variables

Based on the above assumptions, we propose a conceptual model as shown in Figure 1. In terms of scale design, the measurement of monetary cost in perceived cost is based on the scales of Sánchez [70]. For energy cost and psychological cost measurement, we refer to to Rhama [42] and Ghassani [43]. In terms of perceived value, functional value and social value, we refer to the scales of Sánchez [70] and Sweeney [32], and our measurement of knowledge value refers to the study of Babin [71]. Positive emotion refers to the studies of Schoefer [72]. Travel intention was measured by Chen [19]. On the basis of the reference and definition of variables, the initial scale of this paper is formed and the questions are modified by the relevant experts to delete opinions, and finally the initial questionnaire is formed. In order to better test the reliability and effectiveness of the questionnaire, the research team conducted a field presurvey in Guangzhou from November, 2019. A total of 80 questionnaires were distributed and 75 were recovered, of which 68 were valid. Through reliability and validity analysis, it is found that all the measurement indexes in the questionnaire meet the test requirements, and the scale is reliable and scientific, which can be used for formal investigation and subsequent analysis. The formal questionnaire includes some instructions: The first part is the introduction of information, the respondents in the Guangdong–Hong Kong–Macao Greater Bay Area tourism experience survey. The second part is the questionnaire part. After preinvestigation, the latent variables of perceived cost composed of three significant variables, namely, monetary cost, energy cost and psychological cost, the latent variables of perceived value composed of three significant variables, namely, functional value, social value and knowledge, and the latent variables of positive emotion and travel intention, including 25 items. Using the Likert 7-point scale, ‘1’ to ‘7’ indicates a gradual change from ‘totally disagree’ to ‘totally agree’. The third part is the personal information of respondents, including gender, age, income, education and occupation.

4.2. Procedure and Sample

The research team collected data in January 2020 by questionnaire survey. In order to reduce the deviation of common methods, the sample data came from two parts. The first part was the network survey. The data were collected through the questionnaire star APP. The samples were mainly distributed in Guangdong, Beijing, Shandong, Shanghai, Jiangsu and Tianjin. A total of 320 questionnaires were recovered, including 9 invalid questionnaires and 301 effective questionnaires. The second part was on-the-spot investigation. By issuing questionnaires near important scenic spots and landmark buildings in the inner cities of the Guangdong–Hong Kong–Macao Greater Bay Area, such as Baiyun Mountain, Guangzhou Tower, Hong Kong Ocean Park, Macau Samba Square, Shenzhen’s Window of the World, Foshan Zumiao Temple, etc., 230 copies were distributed and 220 copies were recovered, including 8 invalid questionnaires, 222 valid questionnaires, for a final total of 523 valid samples. In terms of demographic characteristics, there were 277 male respondents, accounting for 52.96%, while there were 246 female respondents, accounting for 47.04%, so the gender ratio was relatively uniform. The respondents were widely distributed across all ages, mainly concentrated in the ‘25–44 years old’ and ‘15–24 years old’ categories; 218 people and 135 people, respectively, accounting for more than 65% of the total together. The number of respondents over 45 years old was also higher, reaching 131 people, accounting for more than 25%. The monthly income level of the respondents was relatively uniform, with ‘3501–6000 yuan’, ‘1001–3500 yuan’ and ‘6001–8000 yuan’ accounting for 31.36%, 24.28% and 23.14% of respondents, respectively. Respondents’ education level distribution was more uniform, mainly ‘junior college’, ‘high school and below’ and ‘undergraduate’ accounting for more than 87%, meaning 457 people. The occupational distribution of respondents covered a comprehensive range, with the highest number being ‘enterprise staff’, reaching 147, accounting for more than 28%. In addition, the ‘students’, ‘civil servants/institutions’ and ‘military/armed police/public security’ made up more than than 10%, respectively. A total of 53.33% of the respondents had visited the Guangdong–Hong Kong–Macao Greater Bay Area, 279 respondents, and the remaining 46.67% had not visited the Guangdong–Hong Kong–Macao Greater Bay Area, a total of 244. Respondents often chose to visit Guangzhou, Shenzhen, Hong Kong, Macao and Zhuhai and other international cities, first-tier cities and high-profile tourist cities.

5. Results

5.1. Reliability and Validity

Reliability analysis is used to identify the degree of reliability of quantitative data, which is generally tested by internal consistency coefficient. As

Table 1. Reliability and validity analysis.

Latent Variable/Dimension and Item	Factor Loadings	Average Variance Extracted	Composite Reliability	Cronbach’s α
Overall model (χ2/df = 1.540, GFI = 0.945, AGFI = 0.928, SRMR = 0.027, RMSEA = 0.032, NFI = 0.957, RFI = 0.948, IFI = 0.985, TLI = 0.981, CFI = 0.984)				0.927
Perceived cost (χ2/df = 2.051, GFI = 0.976, AGFI = 0.961, SRMR = 0.021, RMSEA = 0.045, NFI = 0.984, RFI = 0.976, IFI = 0.992, TLI = 0.988, CFI = 0.992)				0.910
Monetary cost		0.781(0.747)	0.915(0.898)	0.898
Monetary cost1: Spend more money	0.885(0.886)
Monetary cost2: More expenditure on scenic spots, transportation and accommodation	0.826(0.824)
Monetary cost3: Difficult to save multiple costs	0.881(0.881)
Energy cost		0.727(0.727)	0.889(0.889)	0.888
Energy cost1: spend more time	0.828(0.826)
Energy cost2: More prone to fatigue and discomfort	0.886(0.886)
Energy cost3: Spend more energy	0.843(0.845)
Psychological cost		0.674(0.674)	0.861(0.861)	0.869
Psychological cost1: To undertake more losses due to decision-making errors	0.827(0.834)
Psychological cost2: Losses resulting from difficulty in selecting alternative tourist areas	0.824(0.828)
Psychological cost3: It is more vulnerable to the loss caused by unpredictable environmental factors such as social security	0.811(0.801)
PERCEIVED VALUE (χ2/df = 2.486, GFI = 0.976, AGFI = 0.955, SRMR = 0.026, RMSEA = 0.053, NFI = 0.973, RFI = 0.960, IFI = 0.984, TLI = 0.976, CFI = 0.984)				0.910
Functional value		0.637(0.637)	0.840(0.840)	0.840
Functional value1: More reasonable and distinctive travel arrangements	0.808(0.807)
Functional value2: Good value for money	0.810(0.813)
Functional value3: Experience quality will be better	0.775(0.773)
Social value		0.614(0.615)	0.827(0.827)	0.827
Social value1: Make more friends	0.745(0.737)
Social value2: Win more praise and envy	0.795(0.794)
Social value3: Get social recognition	0.81(0.819)
Knowledge value		0.613(0.614)	0.826(0.827)	0.827
Knowledge value 1: Get more comprehensive knowledge	0.801(0.795)
Knowledge value 2: Get more tourism information	0.767(0.775)
Knowledge value 3: More cognition of urban cultural characteristics	0.781(0.781)
Positive emotion		0.722	0.912	0.880
Positive emotion 1: Feel happier	0.858
Positive emotion 2: Feel more relaxed	0.830
Positive emotion 3: Feel more comfortable	0.844
Positive emotion 4: Feel more excited	0.867
Travel intention		0.729	0.890	0.912
Travel intention 1: I will continuously visit (revisit) multiple cities	0.851
Travel intention 2: I will recommend to relatives and friends that they continuously visit multiple cities	0.861
Travel intention 3: I will publicize continuous tours of multiple cities through the Internet	0.849

Notes: The goodness-of-fit indicators χ²/df < 3; GFI, AGFI, NFI, RFI, IFI, TLI, CFI > 0.9; SRMR, RMSEA < 0.08, observation model test in parentheses.

shows, the Cronbach’s α value of all variables is 0.859~0.927, indicating that the data reliability is high [73]. At the same time, the composite reliability values of eight first-order factors ranged from 0.826 to 0.915, and the composite reliability values of two second-order factors ranged from 0.827 to 0.898, indicating that the reliability of the overall scale and the second-order observation model was tested.

Since this paper refers to the mature scale, confirmatory factor analysis is used to test the suitability and authenticity of structural validity. As shown in Table 1, the load values of all factors are greater than 0.700, and the fitting indexes of the overall model composed of two second-order observation model and eight first-order factors all meet the conditions, indicating that the fitting goodness of the model is good. In addition, all average-variance-extracted values ranged from 0.613 to 0.781, indicating that the convergent validity of the scale was tested. Moreover, as shown in

Table 2. Total model and observation model discrimination efficiency.

	Average Variance Extracted	1	2	3	4	5	6	7	8
1 Monetary cost	0.781 (0.747)	0.884 (0.864)
2 Energy cost	0.727 (0.727)	0.659 (0.659)	0.853 (0.853)
3 Psychological cost	0.674 (0.674)	0.643 (0.643)	0.656 (0.655)	0.821 (0.821)
4 Functional value	0.637 (0.637)	0.363	0.346	0.512	0.798 (0.798)
5 Social value	0.614 (0.615)	0.370	0.406	0.557	0.626 (0.627)	0.784 (0.788)
6 Knowledge value	0.613 (0.614)	0.370	0.323	0.539	0.638 (0.639)	0.629 (0.634)	0.783 (0.784)
7 Positive emotion	0.722	0.199	0.177	0.293	0.458	0.547	0.563	0.850
8 Travel intention	0.729	0.047	0.131	0.276	0.423	0.512	0.563	0.850	0.854

Notes: On the diagonal is the square root of the average variance extracted value of each variable, and below the diagonal is the correlation coefficient between the variables (observation model test in parentheses).

, the correlation coefficients between all variables are less than the square root of average-variance-extracted value, indicating that the scale measurement results have good discrimination validity. Moreover, perceived cost and perceived value have a relatively stable three-factor model. Monetary cost, energy cost, psychological cost, and functional value, social value, and knowledge can represent perceived cost and perceived value, respectively, for subsequent analysis.

5.2. Common Method Bias Test

For the common method deviation, a series of control procedures need to be taken in the course of the study. This paper ensures anonymity in the investigation process, emphasizes that the answer is correct and wrong, sets the order of the items, improves the scale items, and obtains data sources by channels. However, in order to objectively reflect the problem of common method deviation, this paper uses two methods to test the common method deviation of data according to previous studies [74]. Firstly, the Harman single factor test was used to analyze all items of the questionnaire by SPSS 25.0. The results showed that the variance explanation rate of the first unrotation factor was 36.91%, which was not more than 50%, indicating that there was no obvious common methodological deviation in the research data [75]. Then, the correlation coefficient between latent variables is tested. As shown in Table 2, the absolute value of the correlation coefficient is less than 0.9, indicating that there is no obvious common variance in the data.

5.3. Main Effects

In order to test the main effect between perceived value and travel intention, this paper constructs a multiple regression model with functional value, social value and knowledge value as independent variables, travel intention as a dependent variable, experience, gender, age, education level, income and occupation as control variables. As

Table 3. Multiple linear regression analysis (N = 523).

	Model1: Perceived Value—Travel Intention				Model2: Perceived Value—Positive Emotion				Model3: Positive Emotion—Travel Intention
	β	SE	t-Value	p-Value	β	SE	t-Value	p-Value	β	SE	t-Value	p-Value
Constant	1.515 *	0.681	2.225	0.027	2.314 ***	0.694	3.334	0.001	−0.051	0.499	−0.103	0.918
Experience	−0.250	0.177	−1.411	0.159	−0.377 *	0.181	−2.087	0.037	0.005	0.129	0.039	0.969
Gender	−0.032	0.129	−0.251	0.802	−0.183	0.131	−1.397	0.163	0.092	0.094	0.982	0.327
Age	−0.022	0.072	−0.301	0.763	−0.034	0.073	−0.460	0.645	0.001	0.052	0.023	0.982
Income	−0.064	0.065	−0.988	0.324	−0.119	0.066	−1.789	0.074	0.016	0.047	0.339	0.735
Education	0.292 ***	0.072	4.033	0.000	−0.052	0.074	−0.706	0.481	0.327 ***	0.052	6.234	0.000
Occupation	0.057	0.033	1.709	0.088	0.023	0.034	0.691	0.490	0.041	0.024	1.700	0.090
Functional value	0.072	0.051	1.417	0.157	0.108 *	0.052	2.084	0.038	−0.001	0.037	−0.029	0.976
Social value	0.246 ***	0.054	4.523	0.000	0.257 ***	0.055	4.627	0.000	0.072	0.040	1.799	0.073
Knowledge value	0.339 ***	0.056	6.090	0.000	0.341 ***	0.057	6.013	0.000	0.108 **	0.042	2.589	0.010
Positive emotion									0.677 ***	0.031	21.568	0.000
R2	0.319				0.316				0.643
Adjusted R2	0.307				0.304				0.636
F	26.702 ***				26.294 ***				92.292 ***

Notes: β—standardized coefficient—SE= standard error; the following are the same; *** indicates p < 0.001; ** indicates p < 0.01; * indicates p < 0.05.

shows, the positive effect of tourists’ perceived social value (model 1, β = 0.246, p < 0.05) on TI is not obvious. Therefore, supposing part H1 holds. Among them, H1b and H1c are established, while H1a is not.

5.4. Mediation Effects

In order to reveal the mediating effect of positive emotion between perceived value and Travel intention, this paper takes functional value, knowledge value and social value as independent variables, travel intention as a dependent variable, positive emotion as a mediating variable, and experience, gender, age, income, education level and occupation as control variables to test the mediating mechanism. As shown in

Table 4. Test of intermediary role (N = 523).

	Mediation Effect	Boot95% CI	Mediating Effect Type
Functional value => Positive emotion => Travel intention	0.073	0.003–0.130	Fully mediation
Functional value => Positive emotion => Travel intention	0.174	0.086–0.225	Fully mediation
Knowledge value => Positive emotion => Travel intention	0.231	0.124–0.274	partial medition

, in the first half of the path, tourists’ perceived functional value (model 2, β = 0.108, p < 0.05), social value (model 2, β = 0.257, p < 0.001) and knowledge value (model 2, β = 0.341, p < 0.001) have a significant positive impact on positive emotion, assuming that H2 is all established. Secondly, positive emotion (Model 3, β = 0.677, p < 0.001) has a significantly positive impact on travel intention, and H3 is also valid. On this basis, the Bootstrap test method was used to analyze the mediating effect. As shown in Table 4, positive emotion in the path of functional value and travel intention, where a and b are indigenous, the Boot95% CI of the mediating effect a*b does not include 0, and the direct effect c’ is not indigenous, indicating that the mediating effect accounts for 100%, which is completely mediated. Positive emotion in the path of social value and travel intention, a and b, are obvious, the mediating effect a*b Boot95% CI does not include 0, and the direct effect c’is not obvious, indicating that the mediating effect type is completely mediated; in the path of knowledge value and travel intention, positive emotion are significant in both a and b. The Boot95% CI of mediating effect a*b does not include 0, and the direct effect c’ is significant in both a and b, accounting for 68.13%, which is a partial mediating effect. Therefore, suppose H4 holds.

5.5. Moderating Mediating Effects

This paper then examines the moderating mediating effect of perceived value. First of all, through the process program condition, indirect effect analysis found that in addition to social value and monetary cost, energy-cost-interaction items are not obvious. Then, the subgroup analysis method proposed by Preacher is used for analysis [76]. It was found that monetary cost (confidence interval [0.3445, 0.5029] [−0.0795,0.0999]), energy cost (confidence interval [0.3535, 0.4990] [−0.0823, 0.1070]) and psychological cost (confidence interval [0.3380, 0.4727] [−0.1274, 0.0714]) played a significant moderating role in the mediating effect of positive emotion between functional value and travel intention (one of the indirect effects of moderating variables under different value conditions was significant, the other was not significant). The results tested by this method are affected by the value of adjustment variable, which is not enough to determine whether there is also a moderated mediation effect on other values. Therefore, this paper continues to use the new judgment index proposed by Hayes in recent years (the confidence interval of the index value must not contain zero, indicating that there is a regulatory mediating effect) for testing [77]. It is found that, in addition to the significant mediating effect of the aforementioned path on the index, the psychological cost plays a moderating role in the mediating effect of positive emotion on social and travel intention. The index is −0.1010 (confidence interval [−0.1121, −0.0231]), and the monetary cost and psychological cost play a moderating role in the mediating effect of positive emotion on knowledge value and travel intention. Index indicators are −0.0489 (confidence interval [−0.0931, −0.0095]), −0.0467 (confidence interval [−0.0919, −0.0060]) also have significant results (

Table 5. The moderating mediating effect of PE under PC (N = 523).

	Model1			Model2			Model3
	Effect	LLCI	ULCI	Effect	LLCI	ULCI	Effect	LLCI	ULCI
Direct Effect	0.0608	−0.0041	0.1258	0.1088 **	0.0377	0.1799	0.1340 ***	0.0605	0.2075
The moderating mediating effect of positive emotion under different monetary cost
M − 1SD = 2.3822	0.4234	0.3445	0.5029	0.3283	0.2522	0.4114	0.3820	0.2965	0.4760
M = 4.2996	0.2125	0.1561	0.2786	0.3218	0.2311	0.4158	0.2882	0.2028	0.3707
M + 1SD = 6.2169	0.0017	−0.0795	0.0999	0.3153	0.1606	0.4697	0.1944	0.0492	0.3308
Index of moderated mediation	−0.1100	−0.1389	−0.0776	−0.0034	−0.0465	0.0370	−0.0489	−0.0931	−0.0095
The moderating mediating effect of positive emotion under different energy cost
M − 1SD = 2.3651	0.4242	0.3535	0.4990	0.3319	0.2522	0.4157	0.3715	0.2911	0.4610
M = 4.2314	0.2141	0.1592	0.2810	0.3351	0.2502	0.4198	0.2988	0.2140	0.3816
M + 1SD = 6.0976	0.0041	−0.0823	0.1070	0.3384	0.1965	0.4744	0.2282	0.0741	0.3641
Index of moderated mediation	−0.1125	−0.1429	−0.0801	0.0017	−0.0421	0.0414	−0.0384	−0.0860	0.0021
The moderating mediating effect of positive emotion under different psychological cost
M − 1SD = 3.0470	0.4059	0.3380	0.4727	0.3662	0.2825	0.4543	0.3579	0.2728	0.4434
M = 4.6794	0.1862	0.1226	0.2563	0.2529	0.1617	0.3517	0.2833	0.1907	0.3638
M + 1SD = 6.3118	−0.0335	−0.1274	0.0714	0.1395	0.0023	0.2946	0.2087	0.0657	0.3310
Index of moderated mediation	−0.1346	−0.1667	−0.1010	−0.0694	−0.1121	−0.0231	−0.0467	−0.0919	−0.0060
Model fitting index
Dependent variable	F		R2	F		R2	F		R2
Positive emotion	23.4507 ***		0.2915	20.4728 ***		0.2643	21.8787 ***		0.2774
Travel intention	111.3829 ***		0.6342	113.2985 ***		0.6381	114.5898 ***		0.6407
Model1: Y = Travel intention, X = Functional value, Mediating-variables = Positive emotion, Moderating-variable = Monetary cost, Energy cost, Psychological cost, Covariates= experience, gender, age, income, education, occupation
Model 2: Y = Travel intention, X = Social value, Mediating-variables = Positive emotion, Moderating-variable = Monetary cost, Energy cost, Psychological cost, Covariates= experience, gender, age, income, education, occupation
Model 3: Y = Travel intention, X = knowledge value, Mediating-variables = Positive emotion, Moderating-variable = Monetary cost, Energy cost, Psychological cost, Covariates= experience, gender, age, income, education, occupation

Notes: LLCI—95% confidence lower limit; ULCI—95% confidence upper limit; *** indicates p < 0.001; ** indicates p < 0.01.

). Therefore, Hypothesis 5 holds, among which Hypothesis 5a,c,d,h–j holds and 5b,e,f do not hold.

6. Discussion

With the diffuse development of city tourism to city agglomeration tourism, the multicity tourism mode represented by self-driving tours has become mainstream. Travelers often prefer to visit multiple cities in a city agglomeration at one time, to pursue different levels of value and physical and mental pleasure by visiting cities with different local characteristics in a certain area. However, visiting more than one city often costs more and even makes the traveler feel that it is not worth it. Therefore, this paper is based on the cognitive–affective–behavioral attitude model and customer-delivered value theory to explore the influence mechanism of tourists’ value perceived value and perceived cost on the travel intention to urban agglomerations. Next, the paper discusses the findings in terms of both theoretical contributions and practical implications.

In terms of theoretical contributions, previous studies have mostly focused on the role of tourists’ perceived value of visiting a single tourist attraction or city on their willingness to travel [78,79], without taking into account the differences in the perceived value of different levels of willingness to travel brought about by multiple cities at once. This paper confirms the relationship between perceived value and travel intention in the context of urban agglomeration tourism, and revealing the differences in the impact of functional value, social value and knowledge value on travel intention. On the whole, perceived value has a positive impact on travel intention. Among them, functional value, as the most basic level of perceived value, does not actually have a direct causal relationship with travel intention, which indicates that tourists will not directly produce travel intention to continuously visit multiple urban destinations because they realize that they can harvest the lowest level of practical experience. It reflects the new phenomenon of the tourism market in the new era, that is, the demand level of tourists is increasing, and the practical experience represented by the number of tourist cities is not enough to arouse the desire of tourists. Meaning, tourism development should focus more on quality improvement while maintaining scale [80]. As a higher level of perceived value, tourists will perceive social value and knowledge value, which directly produce travel intention. Especially with the improvement of demand level, the positive impact of knowledge value on travel intention is stronger than that of social value. It further illustrates that the future tourism service should focus on how to meet the tourists’ value pursuit of social identity, curiosity and thirst for knowledge in the process of continuous tour of multiple urban destinations. It fully reflects the contradiction between the people’s growing needs for a better tourism life and the unbalanced and inadequate development [81]. The demand for mass tourism has been transformed into the demand for a better tourism life, and the tourism aesthetics has shown connotative development [82].

Secondly, previous tourism research has tended to focus on the mediating role of positive emotions in a single pathway of cognitive attitudes and behavioral intentions [83], with very limited information on the mechanisms by which positive emotions are transmitted. This paper discusses the different effects of the intermediary role of positive emotion in different paths, verifies the good applicability of the cognitive–affective–behavioral attitude model, analyzes the transmission mechanism of different levels of consumer value through positive emotion to travel intention, and reveals the different intermediary types of positive emotion. As a whole, positive emotion has a significant mediating effect between perceived value and travel intention. Among them, positive emotion play a full mediating role between functional value, social value and travel intention, and a partial mediating role between knowledge value and travel intention. On the one hand, whether it is functional value, social value or knowledge value, they all have a positive effect on positive emotion. With the increase of value level, the positive impact of functional value, social value and knowledge value on positive emotion increases in turn, indicating that for continuous visits to multiple urban destinations, tourists perceive the higher level of perceived value, the stronger positive emotion can be formed. On the other hand, positive emotion has a significantly positive impact on travel intention [58], which confirms a strong relationship between positive emotion and travel intention. Furthermore, positive emotion plays a complete mediating role between functional value, social value and travel intention. It can be seen that there was no direct relationship between functional value and travel intention. Under the intervention of positive emotion, the two are organically linked and play a key role. It shows that emotion is an important intermediary variable for low-level perceived value to stimulate travel intention. The perception of low-level perceived value brought by a continuous tour of multiple urban destinations can promote the generation of travel intention only when it is sufficient to arouse the positive emotion of tourists. In the path of social value and travel intention that have direct effects, the mediating effect of positive emotion directly replaces the original direct causal relationship after intervening in the path between the two. This shows that the cognitive–affective–behavioral attitude model plays a key role in the relationship between the low- and middle-level perceived value and travel intention. It shows a strong and stable effect through the effect of emotion-induced behavior, which confirms that emotion has stronger explanatory power than cognition on personal intentional attitude [14]. In addition, positive emotion plays a partial intermediary role in the path of knowledge value and travel intention, indicating that high-level perceived value often does not need to rely on the transmission of positive emotion to directly promote the generation of travel intention, which is manifested as “a trip without plan trip”.

Third, previous studies have mostly included cost in the value dimension, and thus have not been able to explore the interaction between “gains” and “losses” [25], meaning they have not been able to reveal the combined effects of considering both value and costs when tourists are faced with the choice of where to go. This paper confirms the mechanism of customer-delivered value [15], and opens the black box of the interaction between consumer cost and consumer value and its mediating effect on positive emotion. Compared to the results of Ahn’s revisit intention study of green hotels in Malaysia, which focused only on the effects of perceived value and perceived cost on emotion separately [84], this study focused more on the combined effect of the interaction of perceived value and perceived cost on positive emotion. Overall, the perceived cost partially moderates the mediating effect of positive emotion between perceived value and travel intention. Monetary cost, energy cost and psychological cost negatively moderate the mediating effect of positive emotion between functional value and travel intention. Among them, the negative mediating effect of monetary cost and energy cost is equivalent, while the negative mediating effect of psychological cost is stronger than that of the former, indicating that for continuous visits to multiple urban destinations, tourists have lower positive emotions due to their strong perception of psychological cost, thus inhibiting their willingness to travel. This is further manifested in environmental factors, particularly in an environment of uncertainty due to climatic meteorological conditions and pandemic risk, where tourists fear that poor decision making will make it difficult for them to find alternative combinations of urban destinations that can be substituted [66,67]. The practical experience that leads consumers to choose urban destination combinations reduces the impact of positive emotion and inhibits the impact of positive emotion on travel intention. Moreover, psychological cost also negatively moderates the mediating effect of PE between social value and travel intention. It shows that psychological cost makes tourists produce less-positive emotion, thus inhibiting the tourists’ willingness to travel. This is again manifested in tourists’ concern that they will face difficulties in finding alternative urban destination combinations in time amidst unpredictable environmental factors due to decision-making errors, meaning the positive impact of social satisfaction on positive emotion of tourists in the process of tourism is reduced, and the positive impact of positive emotion on travel intention is inhibited. Finally, monetary cost and psychological cost also negatively moderate the mediating effect of positive emotion between knowledge value and travel intention. The moderating degree of the two is equivalent, but the effect is significantly weakened compared with other mediating effects, indicating that monetary cost and nonmonetary cost represented by psychological cost slowly inhibit tourists’ curiosity and thirst for knowledge about new things, and thus slowly inhibit the positive impact on positive emotion and travel intention. It further indicates that no matter the level of consumer value pursuit, it is still affected by monetary expenditure, risk, substitution and environmental factors to varying degrees.

In terms of practical insights, this paper demonstrates, from a demand-side perspective, that the design of tourism boutique routes based on multicity destination combinations is feasible in promoting tourism integration and building regional tourism brands in the context of high-quality tourism development, and has practical guidance for decision makers (government, enterprises and residents) in urban cluster tourism destinations.

Firstly, the exploration of travel intention is an important reference for decision makers to carry out tourism development strategic planning and tourism product planning. Before the generation of travel intention, tourists pay great attention to the amount of value harvested to reflect their pursuit of different levels of tourism value. In the new era, tourists have higher and higher requirements for the diversified, deep-seated and connotative experience of urban agglomeration tourism destinations, and pay more attention to the utility of social value and knowledge value. Therefore, in the linkage development of each city in the urban agglomeration, whether it is in the formulation of tourism theme lines or the creation of tourism series products, it is necessary to do a good job in the innovation and differentiation of tourism content, which is rich in historical connotation, local characteristics and fashion creativity. It is also necessary to do a good job in online and offline publicity. In particular, it can be widely disseminated through short videos and other We-Media means to enhance visibility and organize activities to promote tourists to participate in the publicity of regional tourism brands, so as to achieve the effect of value cocreation, so as to enhance tourists’ social relations and social status, meet their thirst for knowledge, and bring the strongest value to tourists.

Secondly, we should pay attention to the transmission of positive emotion in tourism. There are significant differences between tourism supply and demand in terms of quantity, structure, quality and time and space. Under the background of tourism, the buyer’s market is complex. Tourists produce positive emotion through perceived value, so as to promote travel intention and stimulate the symbiotic effect. As the main body of high-interactive service industry, tourism destination enterprises should pay attention to the management of the service process. Front-line service personnel should communicate and interact directly with customers in the service process. They should understand the needs of consumers in a timely manner, provide the services they can and establish a good image for enterprises [85]. Their attitudes and behaviors also directly determine the perceived service quality and satisfaction of consumers [86]. Tourists can create positive emotion if they can establish good interpersonal relationships with service personnel and experience inner pleasure. In addition, as an environmental factor, the hospitality shown by residents in tourist destinations can make tourists enjoy themselves and obtain good consumption experience, and promote their image perception and satisfaction evaluation of tourist destinations [87].

Moreover, perceived cost, as an important influencing factor, makes tourists subject to monetary costs, energy costs and psychological costs to different degrees when identifying perceived value. To further reflect the development of the tourism market in the new era, ‘saving money, labor and heart’ is still an important factor in the decision-making process of tourists. Specifically, for the functional value, it should be considered from all aspects such as reducing the monetary cost, energy cost and psychological cost. For example, enterprises should formulate detailed and accurate tourism promotion plans, give tourists preferential fees such as tickets, simplify the business process of multidestination tickets, improve the connection system of turnover traffic, reduce the waste of time in the process of travel and prepare life-saving supplies and first-aid drugs to avoid substantial energy loss. By strengthening the tourism insurance system, it can provide tourists with security and practical value. For social value, in order to solve the problem accurately from the perspective of reducing psychological cost, the government should strengthen the construction of local infrastructure and tourism public services, enterprises should strengthen the construction of tourism service facilities and governments and enterprises should cooperate fully to provide attentive services for tourists, so as to avoid risk cost and give tourists more confidence, so that they will not regret choosing to travel and prevent them from generating the idea of replacing tourism destinations. In addition, we should pay attention to the construction of tourism security environment and strengthen the construction of emergency management mechanism. Especially for the global sudden health situation represented by COVID each subject should be flexibly adapted to maintain regional harmony and stability, reduce the cost of scenarios, improve the perception of sociality value, and stabilize the positive emotion of tourists on the basis of compliance with the government’s epidemic prevention policy. For knowledge value, it should be addressed from the perspective of reducing monetary cost and psychological cost. It is necessary to create experiential and immersive tourism products to satisfy tourists’ curiosity, inquisitiveness and infinite desire for knowledge, so that tourists can feel that they are saving money and a sense of accomplishment, and achieve the idea of “the trip has been well worthwhile”.

7. Conclusions

This study takes the Guangdong–Hong Kong–Macao Greater Bay Area urban agglomeration as the background to build a ‘one-way multi-station’ high-quality tourism route, and proves that, compared with a one-time tour of a city, a one-time tour of multiple cities can enable tourists to obtain a greater number of different levels of perceived value, including functional value, social value and knowledge value. These values perceived by tourists can directly promote their willingness to travel to multiple cities. With the increasing connection between emotion and cognition and behavior, perceived value stimulates tourists’ willingness to travel by promoting positive emotions. It is worth noting that travel to multiple cities is also accompanied by high time, energy and psychological costs. In particular, psychological costs, represented by risk costs, switching costs and situational costs, are more likely to affect the positive emotions of tourists. This provides important practical insights for the next phase of strengthening tourism development in urban agglomerations. In the tourism-linkage development of cities within the urban agglomeration, the innovation and differentiation of tourism content should be well done to meet the value demands of tourists at different levels. Frontline service personnel and local residents should show good service attitude and hospitality, respectively, so that tourists can feel warm. In addition, the government and tourism enterprises should formulate preferential policies for tourism, build tourism infrastructure and public service facilities, improve tourism insurance systems and emergency management mechanisms, and create immersive tourism products.

Although this paper discusses the influence mechanism of perceived value and perceived cost on the travel willingness of urban agglomerations, it has made good theoretical and practical contributions. However, some limitations remain. First of all, this paper adopts cross-sectional data for research. In future research, we suggest adopting longitudinal research methods or experimental methods to better explain the causal relationship in the study. In addition, since this study contains data collected in the early stage of the pandemic, although the impact of risk factors such as sudden events is taken into account, it is not expected that the new coronavirus has a strong and lasting leading role. We suggest that future research can focus on analyzing the impact mechanism of psychological costs represented by risk costs, conversion costs and situational costs on the travel intention of urban agglomerations.