Abstract
Urban river tourism can benefit considerably from nighttime business; however, few studies have examined tourist loyalty in the context of urban waterfront night cruises (UWNCs). This study aims to investigate how perceived value influences UWNC loyalty by conducting a fuzzy set qualitative comparative analysis of 372 questionnaires collected from cruise patrons. This study reveals that the loyalty of nighttime tourists is influenced by seven experience variables: nighttime architecture, lighting, nighttime cruising, urban development and image, urban atmosphere, function, and emotion. Five different combinations of the seven variables were found to influence tourist loyalty at various levels. The findings of the present research will improve the general understanding of the urban river tourism experience and contribute to the literature on nighttime tourism, helping destination managers and policymakers with the design, management, and development of nighttime tourism.
Keywords
Introduction
In 1972, public leisure time began to shift from daytime to nighttime (Szalai, 1972). In the 1990s, Britain first put forward the concept of the “24-hour city,” encouraging a series of economic, social, and cultural activities at night in urban downtown areas (Lovatt, 1994). Governments created conditions promoting nighttime tourism to better develop the nighttime economy (Roberts, 2004). For example, scenic areas began providing nighttime accommodations for tourists; museums, toy shops, art galleries, and cultural sites began staying open in the evening (Eldridge & Smith, 2019). Shaw (2018) called the city nighttime a spectacular sight, with the “bright lights, big city” making city nights exceptionally attractive. Illumination art and light projection were used to change buildings’ visual presence. The nighttime economy has developed quickly worldwide, and nighttime tourism products are becoming an important component of that economy because of their unique atmosphere and experiences that can only be created at night (Li et al., 2021)
There has been an increase in nighttime tourism in recent years, both in China and worldwide. One reason is the need to ameliorate the crowding problem that emerges during the day, which inevitably affects the tourism experience. However, urban nighttime tourism has garnered only limited attention from contemporary social scientists (Giordano et al., 2018). Literature on nighttime tourism is rare and the research themes diverse. Mainstream studies of nighttime tourism have addressed:
① Methods for developing and improving nighttime tourism products such as night food tourism (Li & Su, 2021).
② How light festivals play a significant role as a common kind of nighttime tourism, including their economic impacts (Daniels, 2007), experience and perception (Boodnah et al., 2016), propagation mechanisms (Giordano & Ong, 2017), spatial perception (Hawkins & Ryan, 2013), and touristification, such as through hen and stag parties and music events (Smith, 2016).
③ Nighttime tourist behavior (Li & Su, 2021; Hsieh & Chang, 2006), images and perceptions of night market tourism (Leo et al., 2021), comparison of tourists’ perceptions of daytime and nighttime city views (Huang & Wang, 2018), and nighttime tourists’ primary motivation for shopping and intention to revisit (Li & Su, 2021).
The existing literature has a number of research gaps. First, there are many types of nighttime tourism, such as light festivals and nighttime urban tours. Very little has focused on urban waterfront nighttime cruises (UWNCs), which are an important part of the nighttime tourism business. Second, few studies have specifically focused on the experiences of nighttime tourists and their loyalty to nighttime tourism (e.g., whether tourists value nighttime tourism experiences and develop loyalty for nighttime tourism as they do for regular daytime experiences). To fill the aforementioned two research gaps, the present study aims to investigate the factors that cultivate nighttime tourism loyalty.
Urban waterfronts are special areas. They are land-water ecotones that border lakes, rivers, and seas (Papatheochari & Coccossis, 2019). With the expanding of urbanization in recent years, many cities around the world have treated the construction of waterfronts as key municipal projects, regarding them as “popularity-winning.” Numerous special development plans have been made for waterfront areas that are dedicated to environmental protection and waterside deck construction, creating resilient spaces for leisure and recreation. Thus, the study of UWNCs is of great importance, especially for enhancing tourist loyalty and residents’ quality of life through financial returns, popular issues in academic circles and the tourism industry (de Oliveira-Santini et al., 2018).
This study describes this special type of tourism and explores how perceived value influences tourist loyalty to UWNCs, improving the general understanding of the urban river tourism experience and contributing to the literature on nighttime tourism. To achieve these goals, this work applies qualitative comparative analysis (QCA) to a Chinese case study of the Pearl River Night Cruise. This work is the first to study the dimensions of the perceived value of the tourism experience of UWNCs, revealing how a combination of dimensions influences the creation of loyalty. The remainder of this research is organized as follows. First, this study introduces its theoretical background and reviews the empirical literature on the perceived value of and tourist loyalty to UWNCs. Next, the Chinese sample is described, as well as its qualitative methodology of fuzzy set qualitative comparative analysis (fsQCA). This is followed by an analysis of the results. Finally, this research draws conclusions about the findings and discusses the theoretical and practical implications.
Theoretical Framework
Cognitive-Affective-Conative Theory
The cognitive-affective-conative framework derived from the hierarchy of effects model can also be used to explain post-purchase behaviors (Lavidge & Steiner, 1961). As Oliver (1999) argued, customer loyalty is created through cognitive knowledge, affective manner, and conative sense, which in turn affect behavior. Consumers’ perceived value is the most important prior variable determining their loyalty (Chen et al., 2021; Leo et al., 2021).
Tourist loyalty is embodied in the frequency and time of participation in the tourism activity. Attitude refers to tourists’ preference for and persistence in using tourism products, which can be generalized as tourists’ willingness to revisit and popularize the tourism destination (Oppermann, 2000). A number of studies have found that tourists’ perceived value of an experience significantly positively impacts their loyalty. For example, Hoon et al. (2010) found that the perceived value of festival tourism experiences significantly positively influenced tourists’ behavior inclination. Ranjbarian and Pool (2015) argued that the quality of the tourism destination and perceived value of the experience influenced tourists’ satisfaction, and that the two factors had positive effects on visitors’ intention to revisit. Rousta and Jamshidi (2020) found that the value of the taste/quality, health, price, related emotions, and prestige all had positive effects on tourists’ recommendation of food tourism destinations.
Thus, differences in tourism type and experience lead to differences in the perceived value of the tourism experience, and varying levels of perceived value have varying effects on tourist loyalty. On the basis of classical theory and related literature, the present study proposed the hypothesis that tourists’ perceived value of tourism experiences would positively affect their tourist loyalty. Furthermore, the generation of a tourism experience can be a complex process involving numerous factors and various exchanges between subjects and objects (Mehran & Olya, 2020).
On the basis of previous studies and existing research gaps, this study employed cognitive–affective–conative theory and QCA to examine the Pearl River Night Cruise to explore how the perceived value of UWNCs affects tourist loyalty.
Night Tourism and Urban Waterfront Development
Night tourism
Night tourism, an extension of regular tourism occurring in the daytime (Chen et al., 2020), has received a significant amount of attention (Smith & Eldridge, 2021).
Scholars have researched various perspectives of nighttime tourism, including city development (e.g., enrichment of local culture; Hsieh & Chang, 2006), negative influences (e.g., violence or crime; Chen et al., 2020), and tourist behavior (e.g., visiting night markets and buying products; Chen et al., 2020). Among the various topics that have been explored, nighttime tourist behavior and the related pros and cons are the most frequently studied (Cheshire, 2013).
Compared to the frequently discussed nighttime tourism products and market, nighttime tourist loyalty is under-explored. Specifically, how nighttime tourists’ experiences influence their loyalty to nighttime urban waterfront tourism has been neglected. Therefore, further exploration of the topic is needed to fill this research gap and provide practical recommendations for the sustainable management of nighttime tourism products (Chen et al., 2020).
Urban waterfront tourism
River tourism is a special type of tourism product. Rivers are tourist attractions, but also serve as an important traffic corridor and location for water activities (Prideaux & Cooper, 2009; Rhoden, & Kaaristo, 2020). Urban waterfront tourism is a common type of river tourism, referring to tourism activities occurring in urban waterfront areas, such as sightseeing, entertainment events, health and fitness activities, vacation locations, cultural edification, shopping, and other forms of recreation. These activities can be classified into two groups: waterfront shore excursions and water cruises. Water cruises mainly refer to cruise tours, which are based on tourist attractions existing along a fixed cruise route and include city views, cultural and historical sites, recreation destinations, and so forth. Related studies have mostly explored the development and planning of urban waterfront tourism resources (Breen & Rigby, 1996; Tomej & Lund-Durlacher, 2020), associations between waterfront tourism development and real estate development (Speake & Kennedy, 2019), contribution of waterfront tourism to city prosperity (Keyvanfar et al., 2018; Osman & Farahat, 2018), simultaneous positive and negative effects of waterfront tourism on the regional economy (Guo et al., 2017), and creative management of cultural heritage tourism in waterfront communities (Sakdiyakorn & Sivarak, 2016).
The above research provides useful perspectives for understanding UWNCs, but they are mainly concerned with waterfront shore tourism rather than cruise tourism, much less about nighttime water cruises that might offer tourists a novel tourism experience. Thus, this type of tourism research requires further exploration.
Perceived Value and Tourist Loyalty
With regards to tourist behavior, previous studies have illustrated that visitors’ perceived value of a destination represents their understanding and evaluation of the experience provided by that destination, and perceived value serves as an antecedent that influences their loyalty (de Oliveira-Santini et al., 2018; Jamal et al., 2019; Jiang & Hong, 2021). Normal regression analysis and structural equation modeling are the two main methods applied in these studies to test the relationship between the perceived value of a tourism experience and tourists’ loyalty to it (Ahn & Kwon, 2019; Cui et al., 2019; de Oliveira-Santini et al., 2018; EI-Adly, 2019; Fu et al., 2018). Although many empirical studies have explored the relationship between perceived value and loyalty (Chen et al., 2021), two research gaps remain. First, regression analysis and structural equation modeling are both linear test models that focus either on the special net effect of a single variable or present the linear effect of multiple variables. However, the perceived value of an experience not only indicates a linear relationship, it also involves reciprocal and synergetic effects (Correia et al., 2019; Foroudi et al., 2016; Woodside, 2014). Therefore, this mechanism requires further research. Second, UWNCs are an important component of urban nighttime tourism, but the existing literature lacks research on how UWNCs help gain tourist loyalty. Yu and Zeng (2019) argued that tourists’ perceived experience with UWNCs is composed of nighttime architectural experience (NAE), lighting experience (LE), night cruise experience (NCE), urban development and image experience (UIE), urban atmosphere experience (UAE), functional experience (FE), and emotional experience (EE), so more research is needed to reveal how the perceived value of tourism experience influences tourist loyalty.
Research Design
Sample
The Pearl River Night Cruise is a typical UWNC run in Guangzhou, China. It was established in 1960 by the Guangzhou Passenger Liner Company. The tourism route begins at the White Swan Pond in the Pearl River (in the Guangzhou portion) and ends at Pazhou, for a total length of 23.24 km. The route passes the cores of both the old and new urban areas, where tall buildings stand and bright lights shine at night. From 18:30 to 23:00, the Pearl River Night Cruise can be enjoyed from tour ships (usually two to three floors high, with a minimum capacity of 28 and a maximum capacity of 628 passengers, though most carry about 300). Cruises are available every night (20 cruises in the peak summer season, and only a few in the winter), except in seriously bad weather (e.g., typhoons); each cruise lasts 50 to 80 minutes. In recent years, the tourism industry in the area has attracted about three million visits per year. The Pearl River Night Cruise is one of the Eight Scenes of Guangzhou, and the most important element of Guangzhou’s urban tourism market (Tang et al., 2019; Figures 1 and 2).
Route of the Pearl River Night Cruise.
Guangzhou city nightscape as viewed from the Zhujiang River.
Methodology
A traditional linear model can explain the reciprocal effects of two or three variables on the resulting variables, but it cannot accommodate a reciprocal analysis of more than four variables. Based on Boolean algebra and set theory, American sociologist Ragin (2009) developed QCA. This method helps conduct “configuration comparisons” to deal with complex causal relationships, and thus is widely used in social and management studies. In QCA, independent variables are not regarded as having independent effects on dependent variables. Instead, this method evaluates the consistency and coverage of different independent variable combinations and chooses the logical condition combination that offer the best explanation, so as to obtain multiple causal condition configurations (Ragin, 2009). If the result can always occur with or without a certain independent variable, then n independent variables can produce 2 n combinations. For example, this study addressed seven independent variables, so theoretically it could produce 27 combinations that could influence the result variable. However, not every combination can influence the result variable. In such cases, a very influential causal condition configuration needs to be obtained by calculating the consistency and coverage. In short, this provides an evaluation of how multiple concurrent causalities can lead to the same result. QCA can be classified as clear set qualitative comparative analysis, multiple set-valued qualitative comparative analysis, or fsQCA.
The fsQCA process in research has gained significant attention from scholars of various disciplines, due to the high degree of complexity that can be captured through a testing theory based on contextual conditions rather than studying particular traits of individual variables (Pappas, & Woodside, 2021; Rihoux et al., 2013). In addition, significant paths can be identified, allowing for the development of theories of sufficiency (Woodside & Huan, 2012). Therefore, fsQCA is an important technique for analyzing complicated issues, such as how to discover different configurations of multiple interrelated variables (e.g., Harms et al., 2007), all leading to the same desired output (Pappas & Woodside, 2021). In fsQCA, data calibration values take membership scores between 0 and 1. This study used a 5-point Likert scale, with values ranging from 1 to 5 referring to responses ranging from “strongly disagree” to “strongly agree,” respectively. This comported with the assignment requirements of fuzzy set analysis, and hence this study applied fsQCA in the analysis.
Variable Measurement
In this research, the tourism experience items were obtained from Yu and Zeng (2019) and loyalty items from Lee et al. (2011). From these two references, a measurement scale was formed (see Table 1). A survey was conducted from 12 September to 19 September 2019, during which 428 questionnaires were distributed and 372 retrieved.
Variable Measurement Scale.
Reliability and Validity
In the present study, the SPSS 25.0 software suite was used to analyze the reliability and validity of the developed questionnaire. Cronbach’s alpha (CA), composite reliability (CR), average variance extracted (AVE), and the ratio of √AVE to Max R were used to verify the scale’s overall reliability, construct reliability, convergent validity, and discriminant validity, respectively. In the present study, the CA and CR values were generally greater than the standard threshold of .70, AVE was greater than the minimum acceptable level of .50, and √AVE values were greater than the Max R values (Hair et al., 2017). In addition, the multicollinearity of the scale was measured by using the variance inflation factor (VIF). A VIF value of less than 10 indicates low multicollinearity (Petter et al., 2007). Table 2 reveals a Kaiser–Meyer–Olkin (KMO) value of 0.843, a p-value of < .001, a CA value of .705, an AVE value of .487, a CR value of .749, and a √AVE value less than the Max R value; furthermore, the cumulative variance interpretation of eight common factors with eigenvalues >1 was 64%, and the load of each common factor was >0.5. Therefore, each measurement item had good reliability and validity. After the reliability and validity of the conditional variables and the result variables were tested, the mean value of each variable was calculated and used as the basis for the subsequent data analysis.
Main Reliability and Validity Measures.
Data Calibration
Calibration is the adjustment of index values based on certain standards in order to make the results more explainable (Byrne, 2002). Before the data analysis, the raw data for the condition and result variables needed to be calibrated to a fuzzy set between 0 and 1. Since the data in this study fit the characteristics of a 5-point Likert scale, the research employed the calibration method proposed by Fiss (2011) and Ragin (2009) and used three thresholds (i.e., 0.95, 0.5, and 0.05) to represent full membership, the cross-over point, and full non-membership, respectively. Then, the thresholds of the condition and result variables were calculated using the PERCENTILE.EXC function in EXCEL (see Table 3). Finally, the calibrate (x, n1, n2, n3) function in fsQCA was used to calibrate the data.
Checksum Values of the Variables (n = 372).
Results Analysis
Necessity and Sufficiency of Single-Condition Variables
In QCA, consistency and coverage are used to analyze the necessity and sufficiency of condition and outcome variables. The former indicates the extent to which a condition variable can explain an outcome variable. If the score reaches 0.9, the condition variable is a necessary condition for the realization of the outcome variable. The latter indicates the ratio of cases that a condition variable can explain. The closer the score is to 1, more powerful the explanation is.
In Table 4, the consistency of the EE scores was more than 0.9, making it a necessary condition of tourist loyalty, whereas the other single variables were not necessary conditions of tourist loyalty. Thus, tourist loyalty can be achieved through tourists obtaining EE, but doing so also relies on a combination of other condition variables. Therefore, the various combinations of condition variables required further analysis to determine the different combination configurations that would help achieve high tourist loyalty.
Necessity and Sufficiency of Single-Condition Variables.
Note. “~” indicates logic negation, meaning that the condition variable did not exist.
Condition Combination Analysis
The number of samples was relatively high (n = 372), so the minimum threshold was set as 2, meaning that any condition variable combination would require at least two samples. The coincidence degree threshold was set as 0.9, meaning that the result variable could be 1 only when the coincidence degree score was no less than 0.9. An fsQCA produces three results: complex, parsimonious, and intermediate solutions. The difference in the three results is derived from the different logical remainders they contain. To avoid redundant and counterfactual results, this study selected the intermediate solution (see Table 5) for the analysis.
Condition Combinations for High Tourist Loyalty.
Note. ⊗ = the condition variable does not exist; ●stands = the core condition; • = the auxiliary condition, and a blank space = either the existence or nonexistence of the condition variable.
Refer to Fiss (2011) and Ragin (2009) for additional information.
Table 5 reveals that a high-level of tourist loyalty to UWNCs can be achieved by five combinations. The solution coverage of the five combinations was 0.737, meaning that the explanation of these condition combinations for tourist loyalty reached 73.7%. However, the solution consistency reached up to 0.978, far greater than the threshold. From the perspective of each single factor, EE and UAE appeared in all five combinations, proving that the two variables were highly consistent (0.917 and 0.895, respectively), for analyzing the necessity and sufficiency of the single-condition variables. Further analysis showed that the 2a, 2b, 3a, and 3b condition combinations were consistent in their core conditions but different in their auxiliary conditions. Therefore, combinations consistent in their core conditions were classified as the same type.
In contrast to the single factors that served as the focus of other studies, the five variable combinations examined in the present study represent different strategies for influencing tourist loyalty. Combination 1 consisted of ~NAE, UIE, UAE, FE, and EE. Its coverage was 0.431, which was the lowest among all the combinations. Among the various conditions, UAE, FE, and EE were the core conditions, that is, they were the key elements that generated nighttime tourism loyalty. Specifically, tourists who take UWNCs can enjoy a pleasant urban atmosphere; find their need for relaxation, date- and group-appropriate space, and social communication satisfied; feel comfortable, romantic, and joyful EE; and experience UIE, they will develop a high level of loyalty, even if they experienced poor NAE.
Combination 2 included two sub-combinations: 2a and 2b. Combination 2a consisted of LE, NCE, UIE, UAE, and EE. Combination 2b consisted of LE, UIE, UAE, FE, and EE. The coverages were 0.603 and 0.595, respectively. The core conditions were LE, UAE, and EE, and the auxiliary condition was UIE; the difference was in the two auxiliary conditions, NCE and FE. These two variable combinations shared the same core conditions that were conducive to tourist loyalty, namely LE, UAE, and EE. This result indicates that if tourists can view the lighting landscape along the shores; experience a pleasant urban atmosphere; obtain comfortable, romantic, and joyful EE; experience UIE, enjoy the unique appearance, beautiful decorations, fashionable experience, and dynamic scenery of the tour ship; and satisfy their need for relaxation, date- and group appropriate space, and social communication, they can enjoy a pleasant experience and consequently develop a high level of loyalty.
Combination 3 comprised two sub-combinations, namely 3a and 3b. Sub-combination 3a consisted of LE, NCE, UAE, FE, and EE, whereas Sub-combination 3b consisted of NCE, UIE, UAE, FE, and EE. The coverages were 0.571 and 0.598, respectively. As with the other two combinations, the core conditions were NCE, UAE, and EE, and the auxiliary condition was FE; the difference resided only in two auxiliary conditions: LE and UIE. This result indicates that if tourists can view the cruise landscape; experience a pleasant urban atmosphere; obtain comfortable, romantic, and joyful EE; satisfy their need for relaxation, date- and group-appropriate space, and social communication; see the lighted landscape along the shore; and witness the urban development and positive city image, they will develop a high level of loyalty.
Discussion
The nighttime itself is a core attraction, offering a unique setting that provides a distinctive tourism experience (Eldridge & Smith, 2019). The above analysis shows that nighttime tourists witness a special and distinctive landscape when enjoying the Pearl River Night Cruise, which mainly provides spatial tourism along the cruise route. Tourists experience the architectural landscape along the shore from the comfort of their tour ship. The landscape includes the large residential quarter called Zhujiang Dijing; New Century Business Center; White Swan Hotel, which provides services for business meetings, shopping, accommodations, and entertainment; the historic Memorial Museum of Generalissimo Sun Yat-Sen’s Mansion; the Sun Yat-Sen Memorial Hospital; Sun Yat-Sen University; and Guangzhou Opera House, among others. All of this important architecture and colorful lighting form the architectural landscape.
One unexpected result (including Combinations 1, 2, and 3) indicates that there is no significant connection between good NAE and high-level loyalty. This result is seemingly contrary to common knowledge, but in effect has deeply realistic theoretical reasons. The architectural landscape is a primary physical property of the tourist attractions viewed on UWNCs. Although the physical property is a basic factor in tourists’ experiences, it is often not the core factor. In some types of tourism, the specific atmosphere and situation created by the physical property, as well as tourists’ own subjective constructions (Tung & Ritchie, 2011), often become the core factors of the tourism experience. Tourism experience can be divided into two major types: physical and psychological (Milman, 1998). NAE is basically a kind of physical activity experience based on visitors’ sense organs, so it is a low-level experience. However, tourist loyalty is a tourist’s willingness to revisit and popularize the tourism destination, which embodies their emotion and commitment to the tourism destination (Lee et al., 2011); a single low-level experience cannot be expected to generate tourist loyalty.
Significant factors affecting nighttime tourist loyalty were identified. For example, UAE and EE appeared in all condition combinations, indicating that these two condition variables were necessary for high tourist loyalty. The route space of the Pearl River cruise has always been the core axis of Guangzhou City. It is a space that the Guangzhou government is continually trying to create and display; it is the hallmark of Guangzhou as a modern city. Undoubtedly, UWNCs have become an important way for tourists to experience the prosperity of the city. Besides the architectural, lighting, and cruise landscapes, there is physical scenery comprised of tourist attractions along the route. Thus, the physical scenery is a kind of carrier, and tourists associate it with developing the image of the city. The tourism experience obtained is a kind of UAE. For example, one tourist commented: one of the views in Guangzhou that I want to show my mother and sister is the Pearl River Night Cruise; when the brilliant lights are on in the evening, it is really charming to see the prosperity of the city, its neon lights and feeling of peace (Tourist Comment 1). Moreover, based on the carriers of physical scenery and UAE, tourists can obtain comfortable, romantic, and joyful EE. Respondents commented: the night view during the cruise is very beautiful and the tour ship is extremely splendid and beautiful; it feels carefree to enjoy the night view along the shore in the cool breeze (Tourist Comment 2). The Pearl River Night Cruise has always been worth seeing . . . the nighttime Pearl River view is gorgeous and the beautiful view makes us joyful (Tourist Comment 3). The psychological theory of cognition-emotion-behavior suggests that the behavior of an individual is determined by their emotion. In the case of the Pearl River Night Cruise, tourists’ loyalty behaviors, such as their intention to revisit, making positive comments about the cruise, and recommending the cruise to others, are based on their comfortable, romantic, and joyful EE.
Finally, when it comes to tourist behavior, one unique feature of nighttime tourism is that tourists desire not to feel like strangers, but rather to experience a sense of attachment and belonging (Eldridge & Smith, 2019). This was clarified in this study and could explain why EE is vital to tourist loyalty.
Conclusion
Exploring what experiences tourists can obtain from UWNCs and how the perceived value of such tourism experiences might influence their loyalty is significant to urban waterfront tourism product development and the sustainable development of urban waterfront tourism. The findings of the present study suggest that the value tourists perceive plays a key, multidimensional role in generating loyalty; these findings support the hypothesis proposed in the study. Therefore, based on fsQCA, this study investigated how different dimensions of tourism experiences can be combined to achieve high-level tourist loyalty. The findings of this study are as follows. First, the perceived value of UWNCs consists of seven dimensions: NAE, LE, NCE, UIE, UAE, FE, and EE. These seven dimensions constitute the seven condition variables influencing tourist loyalty. Second, good UAE and EE are necessary for high-level tourist loyalty, but there is no significant connection between high-level tourist loyalty and good NAE. Third, within the solution coverage that had an explanation value of 73.7%, five condition combinations were found to achieve high-level tourist loyalty. Among the five combinations, the one with the most powerful explanation was Combination 2a, which included LE, NCE, UIE, UAE, and EE; the one with the least powerful explanation was Combination 1, a combination of NAE, UIE, UAE, FE, and EE.
This study makes a number of primary theoretical contributions. First, it develops cognitive-affective-conative theory. In earlier research on the relationship between the perceived value of the tourism experience and tourist loyalty, the most prevalent method was quantitative statistical analysis, especially correlation, regression, and structural equation modeling analyses (Hoon, et al., 2010; Oppermann, 2000; Ranjbarian & Pool, 2015). These methods emphasize a linear relationship between a single and multiple variables. However, unlike existing studies, use of fsQCA in the present study allowed researchers to locate several interactive condition variables in the same framework and form a perceived value configuration that could influence tourist loyalty and explore how different dimensions of the tourism experience might combine to achieve high-level tourist loyalty. This not only clarifies the inner logical relationship between the perceived value of the tourism experience and tourist loyalty, it also further enriches theoretical research on the tourism experience.
Second, this study enriches the existing body of tourism research by focusing on nighttime tourism; daytime tourism is a common topic of mainstream research in this area. This study shows that nighttime tourism is different from typical daytime tourism in terms of the setting, experience, and attractions. Specifically, this finding improves upon Yu and Zeng (2019), further proving the reliability and validity of the perceived value scale of urban water NCE and its influence on tourist loyalty.
Third, this study found that with the same research object and situation, five paths can help achieve high-level tourist loyalty. This also advances existing research findings. In tourism research, different condition variables (or factors) exist and influence tourist loyalty, but this always depends on the research object and situation. However, this study found that there is no significant connection between high-level tourist loyalty and good NAE. Instead, EE is necessary for high-level tourist loyalty, and combined with LE, NCE, UIE, UAE, and FE can help achieve tourist loyalty.
This research has some important practical implications. First, the findings will aid policymakers in developing nighttime tourism. Understanding the configuration of the nighttime tourist experience and considering tourist loyalty will make tourism project planning and development more personalized and customer oriented. Second, this study identified the critical role of tourist experience in establishing loyalty, offering useful information for managers of nighttime tourism companies (e.g., cruise companies) seeking to offer exclusive nighttime tourism products and encourage tourist loyalty.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by The National Social Science Foundation of China [No. 21BGL284], and the 2021 Joint Project of the 14th Five-year Plan for the Development of Philosophy and Social Sciences in Guangzhou [No. 2021GZGJ53].
