Sustainable Cultural Heritage Tourism: An Extended ECM Analysis of Destination Performance on Long-Term Tourist Loyalty

Abstract

To identify the impact of destination performance on long-term tourist loyalty in the context of sustainable cultural heritage tourism, this study formulated a research model to examine the relationship between destination performance and perceived value, expectation confirmation, satisfaction, and loyalty through extending the expectation–confirmation model (ECM). Using the Pantang Wuyue Historic District in Guangzhou as a case, data were collected from 542 tourists and analyzed using a structural equation model (SEM). The results indicate that destination performance exerts a direct and significant influence on long-term tourist loyalty. Furthermore, destination performance exerts a direct and significant influence on expectation confirmation and perceived value. The empirical analysis not only provides a comprehensive theoretical framework for understanding tourists’ long-term loyalty in the context of sustainable cultural heritage tourism but also offers practical insights for managers aiming to improve the quality and attractiveness of destination performance to foster long-term tourist loyalty.

1. Introduction

Historic districts represent a unique confluence of cultural heritage preservation and contemporary urban life, serving as increasingly significant tourism destinations worldwide and offering invaluable resources for urban diversity and sustainable development [1,2]. Often characterized by distinct architectural styles, cultural significance, historical narratives, and unique atmospheres, these historic districts attract visitors seeking authentic cultural experiences and connections to the past [3,4]. In recent decades, these historic districts have increasingly become focal points for tourism development, recognized for their potential to attract visitors, stimulate local economies, and foster cultural exchange [3,5].

At present, quite a few historic districts are currently developing tourism products by utilizing local architectural heritage and cultural resources to promote sustainable heritage tourism, cultural tourism, ecotourism, and experiential tourism [6]. Likewise, China possesses vast territories with abundant historic urban landscapes, traditional architecture, and intangible cultural practices, all of which provide significant tourism potential for historic districts [7]. In recent years, with the financial support and policy incentives provided by the Chinese government for the revitalization of historic districts, many successful cases of preservation and revitalization of historic districts through tourism are becoming more and more prominent [8]. Some have gained international recognition, such as Gulangyu Island in Xiamen and Kaiping Diaolou in Guangdong, which integrate Lingnan cultural heritage with sustainable tourism practices [9,10,11]. Additional data show that cultural heritage tourism occupies an important position in the world and contributes significantly to tourism revenues. Cultural heritage tourism in Asia accounted for 38% of total international tourist arrivals in 2019 [12]. In China, historic districts play a key role and contribute significantly to domestic tourism revenues.

As an important part of cultural heritage, historic districts attract a large number of tourists, and previous studies have revealed that destination image is one of the most important factors influencing long-term tourist loyalty. Therefore, there is a close relationship between destination performance and loyalty, and it is necessary and valuable to study loyalty from the perspective of destination performance. However, most existing research on China’s historic districts primarily focuses on preservation policies and their social and economic significance, while very few studies analyze tourists’ perceptions of destination performance, destination image, satisfaction, and loyalty. Destination performance indicators (e.g., infrastructure and service quality) are widely recognized as drivers of loyalty [13], but the research mechanisms linking sustainable destination performance to long-term tourist loyalty in cultural heritage destinations remain underexplored.

To fill this gap, this study looked at how destination performance affects perceived value, expectation confirmation, satisfaction, and loyalty. It used the expectation–confirmation model (ECM) to see how sustainable destination performance influences long-term tourist loyalty in cultural heritage tourism, focusing on the Pantang Wuyue Historic District in Guangzhou as an example.

Broader urban economic development strategies closely link the rise of tourism in historically significant urban areas [14]. On 24 October 2018, President Xi Jinping inspected the old town of Liwan, Guangzhou, where the more than 1000-year-old Pantang Wuyue Historic District is located. When talking about the specific protection and tourism development of historic districts, President Xi Jinping particularly emphasized the improvement in and enhancement of the residents’ living environment through micro-renovation and micro-renewal of historical buildings, as well as the need to tap into the local cultural heritage according to the local conditions and situation so as to perpetuate the historical lineage and, at the same time, present tourists an aesthetic perception and experience. The People’s Government of Liwan District has taken President Xi Jinping’s directive as the highest guideline and has carried out micro-renovation of Pantang Wuyue, presenting a perfect fusion of traditional historical heritage and modern city, which is a successful case of renovation of historic districts.

Since its renovation, Pantang Wuyue Historic District has attracted a cumulative total of 1.12 million visitors. During the National Day holiday in 2024 alone (1 October to 7 October), the main tourist attractions in Liwan District received a total of about 2,411,100 visitors. Pantang Wuyue Historic District, located in the Lizhi Wan scenic area, received 130,800 visitors, an increase of 107.6%. The Pan Tang Wuyue Historic District preserves a wealth of ancient historical sites, such as the pavilion located at the centre of the district (Figure 1—1). At the same time, the daily lives of local residents exude a strong sense of community. There are still four shops selling vegetables, fruits, and meat to meet the convenience needs of local residents (Figure 1—2). During the winter, local residents hang cured meat on utility poles, further highlighting the vibrant local lifestyle (Figure 1—3). Pantang Wuyue Historic District has gradually become a destination that tourists are pleased to choose to visit.

Currently, all activated properties in the first phase of the development have been revitalized, and the second phase’s revitalization rate is over 50%. According to statistics, more than 40 commercial tenants have moved in, with a revitalization rate of 85%. Stores with styles suitable for the historic districts, such as Lacquerware production (Figure 1—4), Chaozhou Gongfu tea (Figure 1—5), Lingnan martial arts (Figure 1—6), guqin and traditional Han Chinese dress, jade and wood carving, etc., add traditional charm to the historic districts. Businesses such as Panxi Restaurant, famous for Cantonese cuisine (Figure 1—7), authentic local snacks (Figure 1—8), and 1200 independent bookstores (Figure 1—9), international youth hostels, and other businesses are injecting new vitality of youth and fashion into the historic districts.

2. Literature Review

2.1. Cultural Heritage Tourism

Cultural heritage tourism involves both tangible and intangible cultural assets [15]. Tangible cultural heritage includes historical sites, archaeological sites, museums, etc. [16]. Intangible cultural heritage includes languages, stories, songs, arts, dances, hunting methods, rituals, and customs [17]. Historic districts are historical sites with tangible cultural heritage and also have intangible cultural heritage, such as songs, art, rituals, and customs. Historic districts are the cornerstones of urban development [18], and they are the places where people in fast-paced urban life long to relax in a tranquil environment and experience the beauty of cultural heritage and architecture [19]. In recent years, with the improvement in living standards, Chinese tourists’ needs are increasingly diverging, as they are no longer satisfied with traditional mass tourism but are turning to experiential tourism that is more culturally immersive and provides restorative experiences [20]. This trend has led to an increasing number of urban residents seeking recreational activities in historic districts for weekend getaways and temporary respite. As an important part of urban space, historic districts have diverse ecosystems that can provide a variety of cultural services to the public [21]. Extensive studies reveal tourists’ motivations for visiting historic districts, driven by benefits such as architectural heritage, authentic cultural narratives, tranquil environments, social hedonism, and affordable travel costs [22]. These findings suggest that tourism in historic districts can satisfy the diverse needs of contemporary tourists.

2.2. Expectation–Confirmation Model (ECM)

Bhattacherjee (2001) developed the expectation–confirmation model (ECM) based on Olive’s (1980) expectation-confirmation theory (ECT) [23,24]. The ECT was initially developed to explain consumer satisfaction with goods and services. Since then, it has been extended and applied to the study of user satisfaction with information technology and systems, where its validity and applicability have been widely demonstrated. The ECT consists of five dimensions: expectation, perceived performance, expectation–confirmation, satisfaction, and repurchase intention. Expectation refers to consumers’ expectations of product or service performance based on advertising, word of mouth, prior experiences, and other factors before purchasing a product or service. Perceived performance is the consumers’ subjective feeling and evaluation of the performance of a product or service after actually using it. Expectation–confirmation is that consumers compare the perceived performance with their previous expectations. Satisfaction is the consumer’s overall satisfaction with the product or service based on the degree of expectation–confirmation. Repurchase intention to use refers to the continued purchase of a product or the continued use of the service [25].

Despite ECT having significant value in understanding consumer satisfaction and continued usage intentions, it is not without limitations [26]. Some scholars have noted that the expectation–confirmation theory (ECT) fails to consider changes in consumers after they purchase products and services, as well as how these changes impact their willingness to continue using them. To address this, Bhattacherjee (2001) proposed the expectation–confirmation model (ECM) [23]. The ECM emphasizes perceived value and continuity of behavior. Recent years have seen its application in the tourism field, primarily to investigate tourist satisfaction, loyalty, and willingness to revisit destinations. Despite the current scarcity of research in this area, existing studies have shown the potential and feasibility of the ECM in tourism research. For example, researchers examined the factors that influence loyalty to tourist destinations during major sporting events, incorporating a modified and expanded ECM [27]. Additionally, an aesthetic expectation–confirmation model was proposed and tested to examine the relationship between aesthetic expectations, experiential qualities, and tourist satisfaction in the Zhangjiajie National Forest Park [28]. It is evident that the ECM is increasingly being applied in tourism research, which is a significant development in the field.

Although no studies currently apply the ECM to cultural heritage tourism, related background research provides a sufficient theoretical basis for this paper. It is worth noting that cultural heritage tourism, unlike general tourism research, involves both tangible and intangible cultural heritage. This makes it a unique form of tourism that combines cultural heritage exchange and protection with tourism activities. Therefore, destination performance influences tourists’ choices of cultural heritage tourism destinations. Destination performance directly impacts expectation confirmation and perceived value. These factors affect destination image and satisfaction. Ultimately, they influence long-term tourist loyalty and willingness to revisit. In the Extended Consumer Behavior (ECM) model, scholars use perceived usefulness or perceived value to replace expectations. However, in the process of establishing long-term tourist loyalty, which is influenced by various tangible and intangible factors, perceived usefulness and perceived value represent a comprehensive assessment of tourists’ perceived benefits and losses. Thus, in this study, the ECM serves as the foundational model for long-term loyalty to cultural heritage tourism destinations. This study treats destination performance as a precursor that influences expectation confirmation and perceived value, building on the research of Chi & Han (2021) [29] and Gao et al. (2025) [27]. These factors then impact destination image and satisfaction. This approach provides a more comprehensive explanation of the development of long-term tourist loyalty behavior.

2.3. Destination Performance, Expectation–Confirmation, Perceived Value, and Loyalty

Destination performance includes perceptions of the destination experience, travel time, travel costs, accommodation costs, health and hygiene, and governmental protocols, all of which have an impact on tourists’ destination choice [30]. Based on the research of Chi & Han (2021) [29] , this study identified seven dimensions of destination performance for the Pantang Wuyue Historic District in Guangzhou: self-improvement and learning, local hospitality, tourist amenities, local cuisine, culture and heritage, slow environments, and local handicrafts.

Self-improvement and learning refer to the unique opportunities that cultural heritage tourism provides visitors for self-improvement and learning. Visitors can broaden their horizons, expand their knowledge, and enhance their cultural literacy by gaining a deeper understanding of the history, culture, and traditions of Pantang Wuyue. Local hospitality refers to how warm and friendly the locals are to visitors during their time in the Pantang Wuyue Historical District. Tourism amenities refer to the development of the accommodation, dining, transportation, internet, and entertainment facilities in the Pantang Wuyue Historical District, which meets the various needs of visitors and enhances their overall experience. Local cuisine is an indispensable part of the tourism experience. It allows visitors to taste unique foods and provides a more profound understanding of local culture and history, making it an important component of a rich and meaningful experience. The Pantang Wuyue Historical District is located in the core region of Lingnan. Due to geographical environment, climate, natural resources, and historical and cultural influences, the district has developed Guangfu cuisine, which is known for its distinctive flavors. Cultural and heritage elements include Guangzhou pottery, embroidery, lacquer art, Cantonese opera, dragon boat racing, and lion dancing. Slow tourism emphasizes relaxation, immersive experiences, and sustainability. Contrasting with fast-paced, overly commercialized tourism models, it aims to allow visitors to truly experience the local culture, environment, and lifestyle in the Pantang Wuyue Historical District [31]. Local handicrafts are an important part of the local culture and carry rich historical and cultural significance [32]. Items such as Guangzhou pottery, embroidery, and lacquer art have unique artistic value and cultural significance. They provide visitors with a more authentic travel experience and allow them to gain a deeper understanding of the local culture and lifestyle [33].

A large number of studies have examined the relationship between destination performance and expectation confirmation. Tourist expectations and tourist experiences have been shown to play an important role in cultural tourism [34]. The expectations that tourists form about the performance of a destination before traveling affect their evaluation of the actual experience. Tourists tend to be satisfied when the performance of a destination meets or exceeds their expectations, and they are disappointed when the destination does not perform as expected [35]. As such, this study proposes the following hypotheses:

H1. 

Destination performance significantly and positively influences expectation confirmation.

Previous studies have also indicated that there is a strong relationship between destination performance and perceived value. Al-Ansi et al. (2019) state that halal-friendly destination performances have a positive and significant impact on the perceived value of Muslim tourists in the context of halal tourism [36]. Perceived value is frequently defined as the tourist’s overall assessment based on perceived utility between what is received and what is paid for in price, time, and effort, which is an important factor influencing satisfaction [37,38]. In accordance with extant research, this study measures tourists’ perceived value of historic districts primarily in terms of three dimensions: emotional value, money value, and social value [27]. Emotional value represents the pleasure, joy, or emotional satisfaction that consumers derive from a product or service [39]. Within the domain of tourism, emotional value can thus be defined as the spiritual well-being and satisfaction that tourists derive from their destination performance. Marpaung et al. (2024) conducted a comprehensive examination of price fairness as a concept related to monetary value, determining that it exerts a substantial influence on satisfaction [40]. The findings indicate that price fairness emerges as the paramount factor influencing tourists’ decisions regarding revisiting. This observation underscores the significance of perceived value, particularly in terms of price, in shaping tourists’ preferences and behaviors. Jia et al. (2024) found that the negative sentiment towards high prices in Macau also emphasizes the practical importance of monetary value [14]. Social value in tourism activities is mainly reflected in the increased social self-concept efficacy that is gained from participating in tourism activities [41]. On the one hand, the positive interaction and sense of accomplishment derived from travel experiences can directly enhance an individual’s perception of self-worth. Another aspect of the participation of tourists in cultural activities is that it increases their sense of social identity and their sense of self-worth [42]. Furthermore, tourism activities facilitate the expansion of social networks and increase social support, thereby improving social adaptability and fostering a stronger sense of social responsibility. If a destination performs well in all aspects, tourists are more likely to perceive that they are receiving a value-for-money experience [43]. Given the empirical evidence, the current study proposes that destination performance significantly influences perceived value. Accordingly, this study proposes the following hypotheses:

H2. 

Destination performance significantly and positively influences perceived value.

In the field of tourism research, destination performance and loyalty represent two pivotal concepts, exhibiting a close interrelationship. Good destination performance is key to developing loyalty. When tourists have a high-quality travel experience at a destination, they are more likely to develop a sense of satisfaction and trust, which in turn creates loyalty to that destination [44]. Therefore, the study proposes the following hypothesis:

H3. 

Destination performance significantly and positively influences loyalty.

2.4. Expectation–Confirmation, Perceived Value, Destination Image, and Satisfaction

When the actual experience exceeds expectations, the perceived value increases significantly. For instance, a restaurant that delivers both exceptional cuisine and service will be regarded by its patrons as offering significant value [45]. Similarly, a destination that surpasses tourists’ expectations will engender high perceived value of the destination. Therefore, the study proposes the following hypothesis:

H4. 

Expectation–confirmation of a destination significantly and positively influences perceived value.

In addition, destination image affects tourists’ expectations of experience confirmation [46]. The quality of the destination image has a direct impact on the expectations of tourists. When the destination image is favorable, tourists have high expectations; conversely, they have low expectations. Therefore, destination image emerges as an antecedent variable in the process of expectation confirmation. Based on the above discussion, this study proposes the following hypotheses:

H5. 

Expectation–confirmation of a destination significantly and positively affects destination image.

Perceived value has a significant impact on destination image. Several studies have shown that tourists’ perceived value of a destination significantly affects their overall image perception of that destination, which in turn affects their satisfaction and loyalty [47,48,49]. Salim and Zhang (2024) found that perceived value had a positive impact on satisfaction [50]. Phi et al. (2024) reiterated the underlying relationship of positive correlation between perceived value and satisfaction in the context of specific cultural heritage tourism [51]. Therefore, the study proposes the following hypothesis:

H6. 

Perceived value significantly and positively influences destination image.

H7. 

Perceived value significantly and positively influences satisfaction.

2.5. Destination Image, Satisfaction, and Loyalty

Destination image is the overall impression that tourists have of a destination, including cognitive image, emotional image, and comprehensive image. A good destination image can attract more tourists and increase tourist satisfaction and loyalty [52]. Destination image, broadly defined as the sum of beliefs, ideas, and impressions that a person holds about a destination, plays a critically important role in tourism, influencing destination choice, consumption behavior, satisfaction, and loyalty. The literature consistently highlights the significant influence of destination image on both satisfaction and loyalty [37,52,53,54,55]. Keni et al. (2018) emphasized that destination image played an important role in affecting tourists’ decision-making processes and intentions to revisit, finding positive direct and indirect effects (via satisfaction) on loyalty [53]. Jeong & Kim’s (2019) study on sports tourists also found that destination images significantly impacted both satisfaction and loyalty, although satisfaction fully mediated the image–loyalty link in their model [37]. Similarly, Marpaung et al. (2024) and Salim and Zhang (2024) identified destination image as a positive influence on both satisfaction and loyalty in their respective contexts [40,50]. Collectively, this body of work provides strong evidence for the strategic importance of cultivating a positive and accurate destination image, particularly one that leverages the unique cultural and historical assets of districts [56], as a method for enhancing satisfaction, fostering attachment, and ultimately driving loyalty and positive behavioral intentions [57]. Therefore, this study proposes the following hypotheses:

H8. 

Destination image significantly and positively influences satisfaction.

H9. 

Destination image significantly and positively influences loyalty.

The link between satisfaction and loyalty (often measured as intention to revisit and/or recommend) is one of the most consistently reported relationships in tourism research, and this holds true across the diverse contexts examined in the reviewed literature. Jeong and Kim (2019) reported a significant impact of satisfaction on loyalty for sport tourists [37]. Sangpikul (2018) found that satisfaction influenced loyalty for island tourists [44]. Alrawadieh et al. (2018) found that overall satisfaction strongly and positively related to destination loyalty at a heritage site [58]. Hallak et al. (2017) confirmed the satisfaction–loyalty link in their higher-order model [59]. Talukder et al. (2024) found that satisfaction strongly related to destination loyalty for eco-tourists [60]. Hence, this study proposes the following hypothesis (Figure 2):

H10. 

Satisfaction significantly and positively influences loyalty.

3. Methodology

3.1. Questionnaire Design

We adopted all research and modeling from previous tourism studies and slightly adapted them to fit the context of tourism in historic districts. The questionnaire design of this study consists of two main parts. The first part was a demographic test designed to collect basic information about the subjects, such as gender, age, and education level. The second part was the latent variable measurement items, expressed using a seven-point Likert scale with a distribution from 1 to 5, indicating a range from strongly disagree to strongly agree. Among them, the second-order latent variable “destination performance” refers to the maturity scale of Chi and Han (2021), which includes seven first-order latent variables, including self-improvement and learning, local hospitality, tourist facilities, local cuisine, culture and heritage, relaxing environment, and local handicrafts, with a total of 19 measurement items [29]. In addition, the second-order latent variable “perceived value” refers to the maturity scale of Gao et al. (2025), which includes three first-order latent variables, including emotional value, money value, and social value, with a total of 11 measurement items [27]. The measurement items of anticipation confirmation refer to the maturity scales of Chi and Han (2021) and Gao et al. (2025), consisting of five measurement items [27,29]. The measurement items of destination image refer to Kim (2018) maturity scale, consisting of 6 measurement items [61]. The satisfaction measurement items refer to Chi and Han’s (2021) and Kim’s (2018) maturity scale, consisting of 4 measurement items [29,61]. Finally, the destination loyalty measurement items refer to Wang et al.’s (2020) maturity scale, which consists of 5 measurement items [62]. All latent variable scales in this study refer to the international maturity scales and were appropriately modified according to specific contexts and research practices.

3.2. Data Collection

This study’s survey questionnaire was collected online via the Questionstar platform, which offers services comparable to Amazon Mechanical Turk. Tourists nationwide who had visited the Pantang Wuyue Historical District in Guangzhou were recruited to participate in the survey. Recruitment channels included the WeChat group of the 1200 Book & Bed Youth Hostel, which has over 500 young tourists who have visited and stayed in the Pantang Wuyue Historical District. Second, on-site questionnaires were conducted at the Pantang Wuyue Party Mass Service Station, where participants scanned a QR code generated by Questionstar. This approach addressed the limitation of the youth hostel WeChat group, which primarily targeted one demographic: Many visitors were elderly individuals or families with children. Both the online and offline surveys offered incentives, such as WeChat red packets and handmade gifts.

The team released the questionnaire on 8 March 2025, and the data collection period ran from 8 March to 28 March, lasting a total of 20 days. We received a total of 548 responses from tourists. Based on three criteria—completion time, completion rate, and consistency in responses to the same question—the team retained 542 valid questionnaires (

Table 1. Demographic information and travel characteristic.

Variable	Category	Distribution	Valid Percentage (%)
Gender	Male	292	53.9
	Female	250	46.1
Age	18–25 years old	90	16.6
	26–30 years old	94	17.3
	31–40 years old	126	23.2
	41–50 years old	182	33.6
	51–60 years old	37	6.8
	Over 60 years old	13	2.4
Educational background	High school or below	55	10.1
	Three-year college	171	31.5
	Bachelor’s degree	110	20.3
	Master’s degree	146	26.9
	Doctorate or above	60	11.1
Occupation	Civil servant/institutional organization	146	26.9
	Company staff	93	17.2
	Self-employed	106	19.6
	Part-time employment	35	6.5
	University student	27	5.0
	Retired	52	9.6
	Unemployed	48	8.9
	Others	35	6.5
Average monthly income	Under RMB 3000	80	14.8
	RMB 3001–5000	159	29.3
	RMB 5001–10,000	93	17.2
	Over RMB 10,000	142	26.2
	No fixed income	68	12.5
Relationship or marital status	Single	76	14.0
	Have a boyfriend/girlfriend	133	24.5
	Married, no children	101	18.6
	Married, with children	174	32.1
	Separated/divorced	58	10.7
Residence	Urban	371	68.5
	Rural	171	31.5
Visit frequency	1 time	213	39.3
	2–4 times	100	18.5
	5–10 times	147	27.1
	More than 10 times	82	15.1
Visit purpose	Escape from busy work and life	267	49.3%
	Come with family or friends to relax	259	47.8%
	Vacationing here with family or friends	268	49.4%
	Watch and experience local traditional festivals or special cultural and artistic events	271	50.0%
	Participate in conferences or exhibitions	391	72.1%
	Visit friends/relatives	314	57.9%
	Other	23	4.2%
Travel companionship	Alone	236	19.2%
	Family/relatives	414	33.7%
	Friends	301	24.5%
	Organized group	168	13.7%
	Business colleagues/partners	110	9.0%
Length of stay	Day trip	176	32.5
	1 night	103	19.0
	2–3 nights	93	17.2
	4–5 nights	78	14.4
	More than 5 nights	92	17.0
Accommodation	Hotel/hostel	394	48.4%
	Homestay/serviced apartment	265	32.6%
	Friends’/relatives’ home	123	15.1%
	Other	32	3.9%
Tourism information channel	Recommendation from friends or relatives	306	30.0%
	TV or radio	111	10.9%
	Internet (Weibo, Xiaohongshu, WeChat, etc.)	395	38.8%
	Newspapers or magazines	49	4.8%
	Travel agencies	156	15.3%
	Other	2	0.2%

).

3.3. Data Analysis

Both SPSS version 24 and AMOS version 23 software were used in this study for further data analysis. As suggested by Anderson and Gerbing (1988) [63], a two-step approach was used in this study. Measurement models were generated by conducting a validation factor analysis using maximum likelihood extraction to examine measurement validity. Then, the proposed theoretical framework was tested by constructing a structural model.

4. Results

4.1. Reliability and Validity Tests

This study first examined the reliability and validity of the scale, where validity was categorized into convergent and discriminant validity.

4.1.1. Reliability Test

Reliability refers to the extent to which the measurement of a variable is reliable, and generally relies on Cronbach’s alpha (CA) and Composite Reliability (CR) for assessment. As shown in Table 1, the CA of each variable in this study is 0.881~0.979, and the CR is 0.884~0.980, which is greater than 0.700. Therefore, the variables have good reliability with their measures.

4.1.2. Convergent Validity Test

Convergent validity indicates the extent to which measures that are theoretically related in the model are actually related, specifically referring to the degree of correlation between variables.

The correlation between the measurement indicators of a variable is mainly tested by using Factor Loading (FL) and Average Variance Extracted (AVE), and the results are shown in

Table 2. Measurement reliability and convergent validity.

Variable	Items	FL > 0.7	CA > 0.7	CR > 0.7	AVE > 0.5
Self-improvement	SI1	0.968	0.964	0.964	0.930
	SI2	0.962
Local hospitality	LH1	0.982	0.967	0.968	0.910
	LH2	0.943
	LH3	0.936
Tourism facilities	TF1	0.980	0.979	0.980	0.923
	TF2	0.951
	TF3	0.956
	TF4	0.956
Local food	LH1	0.982	0.959	0.960	0.923
	LH2	0.939
Culture and heritage	CH1	0.982	0.972	0.973	0.923
	CH2	0.950
	CH3	0.950
Slow environment	SE1	0.980	0.974	0.974	0.926
	SE2	0.947
	SE3	0.960
Local crafts	LC1	0.983	0.969	0.970	0.941
	LC2	0.957
Emotional value	EV1	0.978	0.976	0.976	0.910
	EV2	0.946
	EV3	0.938
	EV4	0.953
Monetary value	MV1	0.972	0.975	0.975	0.907
	MV2	0.946
	MV3	0.951
	MV4	0.941
Social value	SV1	0.980	0.976	0.976	0.932
	SV2	0.962
	SV3	0.954
Expectation–confirmation	EC1	0.974	0.881	0.884	0.609
	EC2	0.718
	EC3	0.767
	EC4	0.701
	EC5	0.707
Destination image	DI1	0.946	0.916	0.919	0.654
	DI2	0.766
	DI3	0.806
	DI4	0.786
	DI5	0.756
	DI6	0.777
Destination satisfaction	DS1	0.958	0.897	0.899	0.692
	DS2	0.805
	DS3	0.760
	DS4	0.790
Destination loyalty	DL1	0.953	0.905	0.907	0.662
	DL2	0.795
	DL3	0.772
	DL4	0.781
	DL5	0.752

. The factor loadings of each measure in the questionnaire of this study are 0.701~0.982, which is greater than 0.700; the Average Variance Extracted (AVE) values are higher than 0.5. Therefore, each variable has excellent convergent validity with its measures.

4.1.3. Test of Discriminant Validity

The degree of discriminant validity indicates the degree of divergence between a particular variable and other variables, which is mainly judged by the comparison between the square root of AVE and the correlation coefficient, and the results are shown in

Table 3. Discriminant validity.

	SI	LH	TF	LF	CH	SE	LC	EV	MV	SV	EC	DI	DS	DL
SI	0.965
LH	0.755	0.954
TF	0.736	0.773	0.961
LF	0.712	0.752	0.776	0.961
CH	0.774	0.718	0.733	0.731	0.961
SE	0.782	0.729	0.738	0.747	0.776	0.962
LC	0.689	0.732	0.744	0.772	0.739	0.723	0.970
EV	0.679	0.658	0.652	0.653	0.678	0.644	0.667	0.954
MV	0.672	0.657	0.671	0.648	0.665	0.634	0.647	0.770	0.953
SV	0.689	0.647	0.621	0.621	0.691	0.638	0.649	0.778	0.763	0.965
EC	0.498	0.451	0.462	0.478	0.532	0.52	0.495	0.512	0.519	0.556	0.780
DI	0.568	0.543	0.535	0.519	0.564	0.541	0.522	0.579	0.592	0.595	0.541	0.809
DS	0.532	0.469	0.464	0.51	0.56	0.542	0.483	0.575	0.569	0.612	0.567	0.565	0.832
DL	0.629	0.557	0.556	0.558	0.639	0.551	0.551	0.59	0.592	0.653	0.513	0.557	0.548	0.814

Note: Bolded data on the diagonal are the arithmetic square root of the variable AVE, and data below the diagonal are the correlation coefficient between the variables.

. The square root of AVE for each variable is greater than the correlation coefficient between the variable and other variables, which indicates that the scale has discriminant validity.

4.1.4. Validated Factor Analysis of Second-Order Variables

According to the table, the absolute values of the correlation coefficients of the factors in the destination performance differential validity test table are high (between 0.689 and 0.782). This indicates that it is appropriate to attribute self-improvement and learning, local hospitality, tourist facilities, local cuisine, culture and heritage, a slow environment, and local handicrafts as destination performance factors, thus allowing the construction of a second-order model. The results of the second-order validated factor analysis of destination performance are shown in

Table 4. Second-order validity factor analysis of destination performance.

Path	FL > 0.7	CR > 0.7	AVE > 0.5
SI → DP	0.860	0.953	0.745
LH → DP	0.861
TF → DP	0.870
LF → DP	0.870
CH → DP	0.863
SE → DP	0.869
LC → DP	0.848

, from which it can be seen that the factor loading of seven sub-dimensions are all above 0.7, with a CR of more than 0.7 and an AVE value of more than 0.5, which indicates that the second-order model is suitable. The hierarchy structure of destination performance in historic districts is shown in Figure 3.

According to the correlation coefficients of the factors in the perceived value distinguished validity test table, the absolute value is high (between 0.763 and 0.778), which indicates that the attribution of emotional value, monetary value, and social value to the perceived value factor is suitable, so the second-order model can be constructed. The results of the second-order validated factor analysis of perceived value are shown in

Table 5. Second-order validated factor analysis of perceived value.

Path	FL > 0.7	CR > 0.7	AVE > 0.5
EV → PV	0.887	0.910	0.770
MV → PV	0.869
SV → PV	0.878

, from which it has been demonstrated that the factor loadings of emotional value, monetary value, and social value are above 0.7, with a CR greater than 0.7 and an AVE value greater than 0.5, indicating that the second-order model is appropriate. Figure 4 displays the hierarchy of perceived value in historic districts.

4.2. Common Method Bias (CMB) Test

This study used a questionnaire survey to collect data. Data collected through this method are prone to common method bias due to the homogeneity of the data. Therefore, statistical control methods were used to test for the presence of CMB in the measurement. First, a confirmatory factor analysis model (M1) was constructed. Second, a model (M2) incorporating a method factor was constructed. Comparing the primary fit indices of models M1 and M2 produced the following results: ΔCFI = 0.013, ΔTLI = 0.013, ΔRMSEA = 0.012, and ΔSRMR = 0.005. Since the changes in all fit indices are less than 0.05, the model does not show significant improvements after incorporating the common method factor. Thus, there is no obvious common method bias in the measurement.

4.3. Model Fit Indicators

The results of the measurement model fitting in this study are shown in

Table 6. Results of model fit test.

Fitness Index	Measurement Model Fit Values	Structural Model Fit Values	Ideal Indicators	Acceptable Indicators
χ2/df	1.746	1.811	<3	<5
CFI	0.977	0.974	>0.9	>0.85
NFI	0.949	0.944	>0.9	>0.85
TLI	0.975	0.972	>0.9	>0.85
IFI	0.978	0.974
RMSEA	0.037	0.039	<0.05	<0.08

. The results show that χ²/df = 1.746 < 3, CFI = 0.977 > 0.9, NFI = 0.949 > 0.9, TLI = 0.975 > 0.9, IFI = 0.978 > 0.9, and RMSEA = 0.037 < 0.05. The above results indicate that the measurement model fit is good.

4.4. Structural Modeling Test

To test the hypotheses, this study first analyzed the fit indicators of the structural equation model. As can be seen from Table 5, χ²/df = 1.811 < 3, CFI = 0.974 > 0.9, NFI = 0.944 > 0.9, TLI = 0.972 > 0.9, IFI = 0.978 > 0.9, and RMSEA = 0.039 < 0.05. The values of the six fit indicators of the structural model of this study meet the acceptable criteria of the commonly used fit indicators of structural equation modeling, indicating that the structural equation model constructed in this study has a good fit. The standardized path coefficients and their significance are shown in

Table 7. Path coefficients and significance results.

Hypothesis	Standardized Coefficient	C.R.	p Value	Support or Not
H1: Destination performance → Expectation–confirmation	0.573	11.864	0.000 ***	Yes
H2: Destination performance → Perceived value	0.773	17.775	0.000 ***	Yes
H3: Destination performance → Loyalty	0.468	8.933	0.000 ***	Yes
H4: Expectation–confirmation → Perceived value	0.174	5.098	0.000 ***	Yes
H5: Expectation–confirmation → Destination image	0.203	4.303	0.000 ***	Yes
H6: Perceived value → Destination image	0.556	10.622	0.000 ***	Yes
H7: Perceived value → Satisfaction	0.540	9.673	0.000 ***	Yes
H8: Destination image → Satisfaction	0.201	3.854	0.000 ***	Yes
H9: Destination image → Loyalty	0.170	3.564	0.000 ***	Yes
H10: Satisfaction → Loyalty	0.178	3.832	0.000 ***	Yes

Note: p *** < 0.001.

. Destination performance has a significant positive effect on expectation–confirmation (β = 0.185, p < 0.001), destination performance has a significant positive effect on perceived value (β = 0.773, p < 0.001), and expectation–confirmation has a significant positive effect on perceived value (β = 0.174, p < 0.001). Expectation–confirmation has a significant positive effect on destination image (β = 0.203, p < 0.001), perceived value has a significant positive effect on destination image (β = 0.556, p < 0.001), perceived value has a significant positive effect on destination satisfaction (β = 0.540, p < 0.001), destination image has a significant positive effect on destination satisfaction (β = 0.201, p < 0.001), destination image has significant positive effect on destination loyalty (β = 0.170, p < 0.001), destination satisfaction has significant positive effect on destination loyalty (β = 0.178, p < 0.001), and destination performance has a significant positive effect on destination loyalty (β = 0.468, p < 0.001), indicating that the hypotheses are all valid. The R² of expectation–confirmation is 32.9% and perceived value is 78.3%, the R² of destination image is 49.0%, the R² of destination satisfaction is 48.0%, and the R² of destination loyalty is 50.8%, indicating that the research model has a good degree of explanation. Figure 5 presents the measurement results for the overall structure model.

5. Discussion and Conclusions

5.1. Discussion

As a unique tourism model integrating cultural heritage and tourism activities, cultural heritage tourism is attracting the attention of an increasing number of tourists. When discussing the future development of this market, it is important to analyze how the destination performance of historic districts fosters long-term tourist loyalty. Research findings indicate that destination performance such as self-improvement and learning, local hospitality, tourist amenities, local cuisine, culture and heritage, slow environments, and local handicrafts are prerequisite factors for forming expectation confirmation and perceived value and also act as constructive elements in enhancing the destination image of historic districts and tourist satisfaction, ultimately influencing tourists’ sustained and long-term loyalty.

Consistent with previous tourism research, this study validates the view that destination performance directly enhances long-term tourist loyalty. For example, Luong (2023) points out that positive destination performance can attract tourists and cultivate their loyalty in tourism [64]. Although previous studies have pointed out that destination performance has an impact on loyalty in normal tourism, this study further validates that the relationship between destination performance and loyalty also exists in the context of cultural heritage tourism. Luong’s article reported a correlation coefficient of 0.320 between destination performance and loyalty, while our study found a higher coefficient of 0.468, further supporting the impact of destination performance on enhancing loyalty in cultural heritage tourism.

However, we must acknowledge that issues such as gentrification, authenticity, and touristification often accompany the revitalization of historic districts. Gentrification refers to the influx of high-income individuals into traditionally working-class or low-income communities, leading to changes in economic, cultural, and demographic structures [65,66]. In this context, authenticity refers to how to preserve the cultural and historical characteristics of these areas during the transformation process [67,68]. Tourismification refers to large-scale renovations undertaken by a region to attract tourists, which can sometimes lead to the dilution of its local characteristics and a focus on serving external consumption [69,70].

There is a complex interplay between gentrification, authenticity, and tourismification. Gentrification may lead to rising housing prices, forcing low-income residents to relocate and altering the social structure of historic districts [71,72]. At the same time, to attract tourists, governments may renovate historic districts to better align with tourist aesthetics [73]. Such renovations may disrupt the original physical spaces and cultural characteristics of historic districts, causing them to lose their authenticity [67]. Urban planners therefore face the challenge of balancing economic development and cultural preservation during the revitalization of historic districts.

Researchers have proposed numerous theoretical models to better understand the relationships between these phenomena. For example, the tourism rent gap theory explains how the influx of tourists leads to rising retail rents, thereby altering the commercial landscape [74]. Additionally, other studies have explored how short-term rentals (such as Airbnb) influence urban transformation through housing assetization strategies [75]. In real-world cases, the complexity of these phenomena becomes evident. Take Pontevedra, Spain, as an example: The revival of its historic center is closely tied to the development of tourism [30]. However, overtourism has also brought negative impacts to the region. In Shenzhen, China, the phenomenon of tourism middle-classification provides deeper insights into the social–spatial consequences of urban revitalization [76].

To address these challenges, urban planners need to adopt comprehensive strategies. On the one hand, they should focus on preserving the original cultural characteristics of historic districts and avoid excessive commercialization [68]. On the other hand, they should prioritize social equity by providing affordable housing for low-income residents to prevent their displacement [77]. Additionally, tourism development must be planned reasonably to avoid overreliance on tourism, ensuring sustainable economic, social, and environmental development [78]. Through these efforts, the revitalization of historic districts can truly benefit all residents, not just a select few.

5.2. Theoretic Contributions

This study examines the mechanisms that influence long-term tourist loyalty behavior toward the destination of historic districts in the context of sustainable cultural heritage tourism by integrating the ECM. In terms of the main theoretical contributions, there are the following: First, this study extends and integrates the ECM with a multidimensional structure, distinguishing it from traditional ECM applications that usually focus on a unidimensional structure. This study integrates the ECM with the multidimensional structure specific to tourism in cultural heritage, particularly by incorporating multidimensional destination performance and perceived value of the destination. This integration provides a deeper and more comprehensive perspective to understand how factors interact and influence destination loyalty in the context of cultural heritage tourism. Second, this study succeeded in identifying associations between destination performance, expectation confirmation, destination image, satisfaction, and loyalty in the context of historic district tourism, reflecting the fact that destination performance can be regarded as an important influence on tourists’ loyalty to a destination through the mediation of destination image and satisfaction. Third, existing research related to cultural heritage tourism does not adequately reveal how tourists’ expectations of confirmation affect their perceptions of historic district tourism. In this study, tourists’ expectation confirmation is revealed to have a moderating effect on the relationships between destination performance and image and between satisfaction and loyalty.

5.3. Practical Implications

According to the questionnaire survey, tourists who live in the city, have a high level of education, and have high incomes prefer to spend time with their families and friends in historic districts, so tour operators can consider creating tourism programs with content and experiences. The following three proposed improvements aim to enhance the tourism experience in Pantang Wuyue Historic District by focusing on its cultural characteristics and heritage resources, specifically in non-heritage handicraft experiences, tourism activity development, and local specialty foods.

5.3.1. Optimize the Non-Heritage Experience

Relying on the existing non-heritage workshops of Guangzhou pottery, Guangzhou embroidery, lacquer art, and Kung Fu tea in Pantang Wuyue Historic District, the “Half-Day Artisan” experience course can be designed to allow tourists to participate in the production of simple versions of non-heritage works (such as hand-pulled embryos and embroidered scented capsules) or experience the process of Kung Fu tea. At the same time, AR technology can be introduced to assist teaching, for example, by scanning the exhibits to show the production process or historical background to enhance interactivity and interest.

A small non-heritage theater can be set up and exhibitions of the skills of non-heritage inheritors and story-sharing sessions can be organized regularly. For example, in conjunction with the Cantonese Opera culture of the Cantonese Opera Art Museum, a short play will be staged to tell the historical origins of the “Pantang Five Xiu” and traditional handicrafts to improve the impact of the culture.

5.3.2. Develop Specialized Tourism Activities

Combining the ancient village pattern of the Ming and Qing Dynasties in Pantang Wuyue with the creative space after micro-remodeling, a “time-traveling” cultural theme line of “Xiguan Time Machine” can be launched. A non-heritage treasure hunt line can be set up by linking jade carving workshops, wood carving studios, and the Cantonese Opera Museum.

We plan to continue the successful Mid-Autumn Festival “Moonlight by the Bay” activities and expand the Dragon Boat Festival, Spring Festival, and other exclusive events. The Dragon Boat Festival can launch a “dragon boat experience + rice dumpling making” experience package, and the Spring Festival can launch a “lion worship + horseshoe cake making” experience package. In addition, the “Pantang Wuxiu” theme bazaar can be organized on a regular basis every weekend, focusing on the exhibition and sale of local traditional ingredients such as horseshoe powder and lotus root products, as well as cultural and creative derivatives.

5.3.3. Deepen the Experience of Local Specialty Food

Collaborations with long-established restaurants such as Panxi and Li Yuan can be carried out to develop a “Pantang Wuxiu” series of dishes (e.g., horseshoe cake, lotus root soup, sweet water of lingjiao), and accompanying cards can be created that explain the connection between the ingredients and Pantang Wuyue.

Using the street space of Pantang Wuyue, local residents can be mobilized to set up mobile food carts to provide affordable traditional snacks (e.g., mung bean paste, congee with woodfish and peanuts, and fried rice noodles) and invite tourists to participate in tasting. It is imperative to enhance the interaction between tourism and the fireworks in the neighborhood and to create a shared food space.

5.4. Limitations and Future Study

Although this study provides valuable theoretical insights and practical implications regarding the impact of tourism destination performance in a cultural heritage context on visitors’ sustained and long-term destination loyalty, it also has several limitations. Firstly, while the study is conducted within the context of sustainable cultural heritage tourism, it only analyzes a single case study in Guangzhou, China. The generalizability of its findings to other cities in China and cultural heritage destinations in other countries may require further validation. Secondly, the study relies on cross-sectional data, which limits its ability to capture the dynamic evolution of visitor behavior over time. Future research could explore tourist psychological and behavioral changes across different experience stages by combining longitudinal data. Additionally, the sample primarily consists of domestic tourists, which may limit the generalizability of the findings to a global context. Future research should increase the sample size of international tourists to support findings related to international cultural heritage tourism.