Destination Image Perception Mediated by Experience Quality: The Case of Qingzhou as an Emerging Destination in China

Abstract

In recent years, the competition in the tourism market has become more and more fierce. Tourism destinations need to ensure they have sufficient sources of tourists, and thus, improving their market competitiveness, image, and reputation are particularly important. For this reason, tourism academia has always attached great importance to the study of tourism destination image. Many studies have shown that tourists’ travel behavior is largely influenced by their perception of tourism destinations. Research on heritage tourism from the supply perspective is relatively abundant, whereas not much research has been conducted on the demand side, and the influence of heritage tourism on the perception of a destination’s image has rarely been discussed. This study examines destination image perceptions through three components: cognition, affection, and quality of experience. We propose a conceptual model that clarifies how the quality of experience mediates the formation of cognition and affection with regard to emerging destinations in mainland China. This study employed the PLS-SEM discontinuous two-stage approach, which involved the examination of higher-order constructs (HOCs). The results from a sample of 475 visitors to the ancient city of Qingzhou in mainland China showed that cognition positively influences affection, and they both positively influence the overall image. Quality of experience was shown to be a mediating factor between cognition and affection. Moreover, the variables under quality of experience were confirmed to be consistent with those under cognition.

1. Introduction

With the increased construction around ecological civilization in China, heritage tourism as a return to nature and history as an important form of cultural tourism has become a hot topic [1,2,3]. Heritage tourism is a tourism activity based on heritage attractions, which include architecture, artworks, natural scenery, and anything else associated with collective memory [4,5,6]. The main focus of the research on heritage tourism has been from the supply perspective, with studies on heritage value attributes, heritage tourism development, the heritage industry economy, and conservation management of heritage, whereas this topic has been less studied from the demand perspective in relation to tourist motivation and destination image [7,8,9,10,11].

Most of the studies on heritage tourism motivation follow two lines of thought: One is centered on heritage tourism sites, with a focus on motivation based on the attributes of the heritage site [12]. It is generally accepted that heritage tourism motivation includes pilgrimage, nostalgia, roots, black tourism, hunting and exploring, and study [13]. Heritage tourism motives also cover mass tourism motives such as leisure and recreation, learning and education, and social interaction [14,15,16,17]. The second is based on tourists’ needs, and the degree of connection to heritage is used as a classification criterion for motivation [18,19,20]. Poria suggests that there is a “core-fringe” structure consisting of motives that are connected to history, motives to learn, motives that are emotionally connected to heritage, and motives that are unrelated to heritage properties [21,22]. It is evident that a visitor’s motivation for participating in heritage tourism is a key factor in understanding heritage tourism; however, heritage tourism is complex and diverse, and there is no consensus on how to evaluate heritage tourism motivation [23].

Destination image perception is defined as tourists’ perceptions, impressions, and emotional expressions of things and phenomena with regard to a destination [24,25,26]. The research mainly includes the formation process, influencing factors, structural characteristics, and the influence of tourists’ destination image perception on behavior and marketing management inspiration [27,28]. In terms of research methodology, the “cognitive-emotional” model proposed by Baloglu and McCleary, which classifies destination images into cognitive, emotional, and holistic images, is widely used in the academic field [29]. In terms of research objects, studies on destination image generally do not distinguish between types of tourist places, and studies on destination image perception mostly focus on general mass tourism; there is a lack of studies on the destination image perception as it pertains to heritage tourism [30,31,32,33,34,35,36].

Past research has shown that tourism motivation is an important psychological factor that drives tourists to engage in tourism behaviors, which in turn, leads to tourism experiences and perceptions of tourism [37,38,39]. Most existing studies have examined heritage tourism motivation and destination image separately, and few have explored the relationship between heritage tourism motivation and tourists’ destination image perceptions; therefore, the question of how complex and diverse the effects of heritage tourism motivation are on tourists’ image perceptions of heritage tourism sites remains to be revealed.

Qingzhou is an ancient city in Shandong Province, China, which has become an emerging destination through the unremitting efforts of local authorities. Recently, the tourist arrivals have increased rapidly. As Butler (1980) mentioned, in the early stages of tourism development, tourists do not have a clear image of the destination [40]. This study tapped into the tourists’ image perception mediated by appraising the quality of the tourists’ experiences in the early stage of destination development, and also added to the knowledge of empirical studies focusing on developed or matured destinations.

2. Hypothesis Development

2.1. Conceptual Framework of Destination Image

It was proposed by Hunt (1975) that image is the potential tourists’ perception of a certain destination. Crompton (1979) dictated that image is “the sum of beliefs, ideas, and impressions that an individual has of a destination” (p.18). It is a person’s mental picture of a specific place [41]. Words frequently used to define destination image include impression, perception, belief, idea, representation, and feeling [42]. Most studies tend to consider destination image as being incorporated into two interrelated concepts: cognition and affection, with affection being greatly dependent on cognition [43,44,45,46]. Cognition is one’s personal knowledge and belief about an object (an evaluation of the perceived attributes of the object), and affection is the overall emotional outcome of appraising an object [47,48,49,50]. It is the expression of positive or negative feelings with intensity [51]. Furthermore, the conative dimension is also a studied component; cognitive image, affective image, and conative image are interrelated, and together, comprise the overall image of a destination [52,53]. The conative dimension is how visitors act towards a destination based on the other two [54]. In other words, it is derived from the previous two factors involving acting, doing, or striving in response to both [55,56]. Finally, a meta-analysis [57] placed perceptions of destination under the cognitive dimension, affective dimension, conative dimension, and overall dimension, and found that the overall and affective dimensions, followed by the cognitive dimension, impose the greatest effect on behavior intention. Generally speaking, the conative dimension belongs to the field of externally oriented behavior research, and thus, does not belong to the field of this study, which focuses on the internally oriented cognitive and affective aspects. Thus, the affective dimension (evaluative component) of the destination image emerges from the cognitive dimension (attributes) and is largely dependent on cognition, with the cognitive component acting as an antecedent to the affective component. Ultimately, the two combine, resulting in a general perception of a destination image. They work together to shape the destination image. Thus, the following hypotheses are developed:

H1. 

Cognition positively influences overall image of a destination.

H2. 

Affection positively influences overall image of a destination.

H3. 

Cognition positively influences affection.

2.2. Experience Quality as a Mediator

2.2.1. Experience Quality

The tourism experience occurs in real time, is dynamic, and is a function of time based on consumption. Therefore, the use of a time-based measurement/real-time measurement method helps us to understand the dynamic experience of tourists [58,59]. In fact, it is the visitors’ longitudinal engagement or participation at the destination that comprises the experience. Different to the experience process, experience quality is an emotional evaluation of the actual experience that occurs afterward. Experience quality as a concept was introduced by [60] for recreational tourism. It was defined as the subjective, emotional, and personal feedback to different aspects of service that results in overall satisfaction. Compared with the technology-oriented service quality, the quality of experience is a requirement for consumer preference [61,62,63] and means more in regard to tourists. Chan and Baum (2007) stated that experience quality can be conceptualized as visitors’ subjective and emotional feedback to the personally desired social–psychological benefits they received. In other words, experience quality is likely to be articulated by tourists when the attraction meets their expectations of fun and fantasy [64,65,66,67]. Destination management organizations (DMOs) should be creative to provide fashionable or favorable products which can benefit the visitors and make visitors feel like they “got it”.

2.2.2. Relationship between Cognition and Experience Quality

Cognition is defined as a personalized internal formation process where valuation is generated from stimulations in the external environment [68]. It is postulated specifically as the information processing of a person’s psychological functions [69,70]. The cognitive system features the interactions of three elements, including the perception of things from the surrounding environment, thought which governs the system, and action to make a choice of what to experience. This process is initiated by the bodily interaction with the environment [71]. As for cognition as applied to destination image, it is an evaluation referring to the individual’s own knowledge and beliefs about the destination. In most survey measurements, the corresponding questions to capture the cognition of respondents are likely meant to capture the beliefs around the performance of attributes through a Likert scale. For example, a question could be “Has interesting festivals”, with answers including: strongly disagree; disagree; neither agree nor disagree; agree; strongly agree. Thus, it is the belief based on knowledge, not emotion, that makes the measurement of cognition different from an emotional measurement.

In the field of marketing, experience as a subjective response by consumers emphasizes emotion and feeling rather than the cognitive dimension [72,73,74,75,76,77]. Experience in a tourism context is the psychological internal reaction created in the process of interaction between visitors and the stimulation of cognitive attributes [78,79], as the cognitive assessment of service cannot be independent of the experience quality, i.e., the emotional or affective assessment. Attributes of the physical environment [80] and intimate services [81] afford opportunities to elicit personal emotional feedback. Through the stages of the process of activity/physical support/social interaction, consumers can use all their senses [82] to evaluate the benefits of cognitive attributes that will significantly cause emotion. It is incommensurate for tourism sector to focus on purely attribute-based components, as experiential benefits remain critical in the process evaluation [83]. The benefits of travel start with the available raw resources or destination attributes [84], and the subjective ratings of the attributes’ performance in one’s personal experience produce the emotional outcome [85,86]. It was empirically confirmed [87] that destination attributes positively affect experience quality. To conclude, the cognition perception of destination image is an evaluation of the destination attributes’ performance from utilitarian or functional standards. Corresponding to the cognitive image, experience quality is the subjective judgement of the performance of attributes that is favorable or not after actually experiencing the destination attributes’ functions. It is proposed that experience quality is positively affected by cognition in the perception of destination image.

2.2.3. Relationship between Experience Quality and Affection

The relationship between experience quality and affection has scarcely been studied. Although affective image and experience quality are both emotional outcomes or evaluations of the visiting process, they are different. Quality of experience is, in particular, referring to the psychological evaluation outcome due to the benefits, favors, or interests that evolved from the experience of attributes. Affection is expressed, however, as the overall positive or negative emotional state or mood, such as sleepy–lively, boring–exciting, distressing–relaxing, and unpleasant–pleasant [87,88,89,90,91], that is felt toward no given object [92], but which is felt with intensity. Researchers studied the influence of in situ visitation on image and found that direct experience can modify both cognition and affection [93,94,95]. Direct and positive effects of a satisfying experience on affective image were further confirmed [96,97]. As the evaluative outcome of the experience process, tourists’ experience quality of a destination significantly affects the affection and cognition toward a destination in a direct way [98], or an indirect way through cognition only [99]. Ref. [100] verified a positive association between a number of the components of the experience quality and emotions. Thus, experience quality is proposed to positively influence affection. Experience quality can be considered as a mediator between cognition and affection in destination perception, as seen in Figure 1.

H4. 

Cognition positively affects experience quality.

H5. 

Experience quality positively affects affection.

H6. 

Experience quality mediates cognition and affection.

3. Research Methods

3.1. Study Setting

Qingzhou is an ancient city with a history of more than 7000 years that is located in Shandong Province, China. It was inscribed on the list of National Famous Historical and Cultural Cities in 2013 by the State Council of the People’s Republic of China. The destination is valued for its “Dongyi culture”, which arose from the Beixin culture, Longshan culture, and Dawenkou culture; other colorful national cultures established by the Han, Hui, Manchu, and other ethnic groups; well-preserved city planning in terms of the layout and landscape; and many religion cultures, including Confucianism, Buddhism, Islam, Christianity, and other religions [101]. As a long-living administrative center at different levels, it is the location where one of the imperial examinations was developed, through which officials at the prefectural level were selected in ancient China. Friendly residents and the highly qualified natural environment marked by mountains and rivers also contribute to the attractiveness of the destination [102]. In particular, Longxing Buddhist Temple, which was excavated in 1996, gained attention from the general public and all Buddhist communities [103] due to its great significance in the study of Buddhism history during the Northern and Southern Dynasties of China [4]. The Buddhist culture of Qingzhou ancient city has had an important influence on Japan and South Korea in Northeast Asia [15].

Based on these abundant attractions, the tourism industry steadily developed and was then greatly accelerated by the nomination of the Qingzhou ancient city attraction as the AAAAA National Tourist Attraction (NTA) in 2017, which is the top level of its kind. The attraction covers the urban center of Qingzhou ancient city, the Yunmen Mountain-Tuo Mountain district, and the Museum district, where the outstanding values of this city are presented for visitors to learn about. As a benchmark of the tourism industry, 5A attractions stimulate the local economy and promote the overall improvement of the destination. Tourist arrivals increased from 4.899 million in 2012 and peaked at around 10 million in 2018 and 2019, with a drop in 2020 possibly due to the COVID-19 pandemic. The revenue from the tourism industry shows a similar trend (Figure 2).

Tourists are mainly from source markets such as Shandong province, where the city is located, as well as nearby northern Chinese provinces and cities such as Hebei Province, Henan Province, Jiangsu Province, Tianjin City, etc. In the future, Japan and South Korea will be the prominently targeted markets. The perception of the domestic visitor to Qingzhou has not yet been scientifically investigated, which has caused a lag in the sustainable development of the destination (local government of Qingzhou, 2014). Additionally, in the early stages of tourism development, tourists do not have a clear image of the place of destination [10]. Thus, it is crucial to conduct research into this field to theoretically and practically contribute to the sustained tourism industry’s development of the destination.

3.2. Instruments and Measurements

The questionnaire covered two parts: in the first part, the data on cognition attributes and the data on the experience quality of the cognitive attributes, affective attributes, and overall image were collected with a five-point Likert scale (where 1 indicates strongly disagree and 5 indicates strongly agree); in the second part, demographic information was collected. Among the cognitive attributes, 23 were adopted from the literature and the rest were self-created by referring to the local tourism management plan. The affective attributes and overall image measurement scale were also adopted from the literature. The draft of the questionnaire was then improved through an indepth interview with 15 pertinent tourism administrators and marketers of Qingzhou ancient city, which led to two self-created attributes and the two corresponding evaluations being deleted. Subsequently, a personally administrated pretest was given to 20 respondents who were purposely chosen from the School of Economics and Management of the local college, Weifang University of Technology, in order to improve the articulation of the questionnaire and help it convey the meaning better. A pilot test of 30 questionnaires for tourists were personally administered by students of Weifang University of Technology who majored in tourism management, with the purpose of enhancing the face validity and intelligibility. At the same time, an online pilot survey of more than 200 respondents through Wenjuanxing (https://www.wjx.cn, accessed on 1 January 2020), one of the largest online survey platforms in China, was administered to probe into the reliability and validity of the questionnaire.

3.3. Sampling and Data Collection

The nonresidential visitors who visit Qingzhou will be the best sources of information that is needed to meet the research objective. This study applied sampling by requiring the students of the Weifang University of Technology to share the two-dimensional code of the questionnaire with their friends and relatives in March, April, and May 2022. At first, 624 observations were processed by deleting the ones with 74% of the same answers. The final dataset was composed of 475 samples, which met the minimum sample size determined by the post hoc power analysis with an effect size of 0.15 and power of 0.95 [7], as well as meeting the criteria set by [18]. Among the 475 samples, 200 (sample 1/S1) were randomly selected for the exploratory factor analysis of the cognition variable, and the 275 remaining (sample 2/S2) were used in the confirmatory factor analysis of cognition. Then, the CFA of experience quality was conducted on the total 475 samples based on the results of the EFA and CFA of cognition.

The demographic profile of the study participants was analyzed using frequency tests. As can be seen in

Table 1. Sample profile.

Variable	Sample (n = 475)	Frequency	Percent
Gender	Male	153	32.21
	Female	322	67.79
Age	<18	9	1.89
	18–39	316	66.53
	40–60	148	31.16
	>61	2	0.42
Educational background	Junior middle school	27	5.68
	Senior middle school	82	17.26
	Undergraduate	352	74.11
	Master’s degree	10	2.11
	Doctoral degree	4	0.84
Occupation	Self-employed	23	4.84
	Public sector employee	270	56.84
	Self-employed	41	8.63
	House wife	116	24.42
	Between jobs	1	0.21
	Student	24	5.05
Annual income (RMB 33,000)	Much more	67	14.11
	More	83	17.47
	Equal to	66	13.89
	Lower	83	17.47
	Much lower	176	37.05
Frequency of visits	First time	276	58.11
	Repeat visit	199	41.89

, the majority were female, accounting for 67.79%. In terms of age, respondents aged 19–39 were the largest group (66.53%), followed by those aged 40–60 (31.16%). The majority of respondents’ education level was undergraduate at 74.11%. In terms of occupation, public sector employees were the largest group (56.84%). In addition, 37.05% of respondents had an annual income significantly less than RMB 33,000 RMB (USD 1 = RMB 6.6994, as of 10 June 2022). Moreover, the frequency of visits for the first time was 16.21% more than repeated visits.

4. Data Analysis

The descriptive analysis was performed by using SPSS version 26, and SmartPLS version 3.3.7 was used to perform the structural equation modeling (SEM) analysis. PLS-SEM functions like multiple regression, but it has more advantages in terms of estimation of the measurement model, as well as having the ability to simultaneously test the structural model with predictable power [2,19], The PLS-SEM approach was adopted by this study since it can cover a complex and broad scope of research, can explore theory sufficiently, and can be used to practice in every field of study (Wold, 1985). In this way, the chosen analytical packages are well suited for the study and can sufficiently explore the potential mediating role of experience quality in the relationship between cognition and affection, in addition to predicting the model.

4.1. Common Method Variance Test

According to [11], obtaining the common method variance (CMV) is crucial for a cross-sectional survey. By using Harman’s single-factor test, the largest variance explained was 47.634 percent (<50%) (Hair et al., 1998) for S1, 48.921 for S2, and 48.238 for all 475 samples. Hence, it is confirmed that the CMV appears to be of no concern, and thus, the data should not distort the meaning involved in the survey measures.

4.2. EFA and CFA of Cognition

Cronbach’s alpha of the 31 cognition attributes of S1 is 0.988; hence, it is confirmed that the data have an acceptable reliability. The KMO of the 31 cognition attributes is 0.968 (>0.7) and the p-value for the Bartlett’s test of S1 is 0.000; hence, it is confirmed that the data can be used in the factor analysis.

The exploratory factor analysis (EFA), which was conducted with the method of principal components based on an eigenvalue greater than 1, was applied on S1, and the three dimensions were automatically appraised. For cognition attributes with loadings of more than 0.4, all of the three dimensions will be deleted, and attributes with loadings of more than 0.5, two of the three dimensions will also be deleted. In this way, 27 of the attributes of cognition remained (

Table 2. EFA of cognition attributes.

Attributes	Dimension 1	Dimension 2	Dimension 3
Facilitating Supply
art cog	0.835
friendly residents cog	0.830
cleanliness cog	0.821
safety cog	0.808
price cog	0.803
ecology cog	0.801
participation cog	0.795
shopping cog	0.787
entertainment cog	0.764
informationalization cog	0.762
hotel cog	0.755
responsible government cog	0.751
festivals cog	0.749
food and beverage cog	0.729
transportation cog	0.727
nightlife cog	0.707
Intangible Attraction
imperial exam cog		0.771
Christianity cog		0.755
Han folklore		0.648
Manchu ethnic group folklore cog		0.731
Hui ethnic group folklore cog		0.636
Tangible Attraction
relics cog			0.760
buildings cog			0.753
forests and mountains cog			0.737
water cog services cog			0.728 0.655
flowers cog			0.632
Cumulative percentage of rotation sums of squared loadings	41.046	63.031	83.103

).

Cronbach’s alpha of the 27 cognition attributes of S2 is 0.984; hence, it is confirmed that the data have an acceptable reliability. The KMO of the 31 cognition attributes is 0.966 (>0.7) and the p-value for Bartlett’s test of S1 is 0.000; hence, it is confirmed that the data can be used to carry out the factor analysis.

Then, S2 was used to perform the CFA. The first run of the CFA showed a not well-built convergent validity in one of the three dimensions; thus, one factor (service cog) with the least standard estimate of the loading coefficient was deleted. For the results, 26 cognition attributes were kept to ensure a good validity (

Table 3. CFA of cognition attributes.

Factor	Item	Std. Error	p-Value	Composite Reliability	Average Variance Extracted
Tangible Attraction	water cog	-	-	0.915	0.684
	buildings cog	0.076	0
	flowers cog	0.072	0
	forests and mountains cog	0.073	0
	relics cog	0.066	0
Intangible Attraction	Hui ethnic group folklore cog	-	-	0.944	0.773
	Manchu ethnic group folklore cog	0.048	0
	Han folklore cog	0.046	0
	Christianity cog	0.050	0
	imperial exam cog	0.051	0
Facilitating Supply	responsible government cog	-	-	0.984	0.790
	festivals cog	0.047	0
	participation cog	0.048	0
	transportation cog	0.052	0
	food and beverage cog	0.046	0
	hotel cog	0.051	0
	entertainment cog	0.046	0
	nightlife cog	0.052	0
	price cog	0.054	0
	informationalization cog	0.048	0
	art cog	0.048	0
	ecology cog	0.047	0
	shopping cog	0.053	0
	friendly residents cog	0.046	0
	safety cog	0.046	0
	cleanliness cog	0.050	0

). Then, 26 cognition attributes of S2 were again assessed with the CFA. Table 3 shows that the constructs are reliable and consistent with the value of composite reliability (CR) above 0.70. Values of average variance extracted (AVE) greater than 0.708 or 0.50 [12] ascertained the convergent validity as well. In

Table 4. Discriminant validity: Pearson correlation coefficient and squared root of average variance extracted (AVE) for cognition.

	Tangible Attraction	Intangible Attraction	Facilitating Supply
Tangible Attraction	0.827
Intangible Attraction	0.818	0.879
Facilitating Supply	0.798	0.840	0.889

, the squared root of the AVE of a certain construct, such as 0.827, 0.879, and 0.889, is greater than the correlation of the construct with all other constructs, showing the accepted discriminant validity of constructs of experience quality [13].

4.3. CFA of Experience Quality

The 26 cognition attributes assessed with the EFA were, accordingly, adopted in the CFA of experience quality by using the total 475 samples. Cronbach’s alpha of the 26 experience quality attributes of the 475 respondents is 0.985; hence, it is confirmed that the data have an acceptable reliability. The KMO of the 26 experience quality attributes is 0.972 (>0.7) and the p-value for Bartlett’s test of the 475 respondents is 0.000; hence, it is confirmed that the data can be used to perform the factor analysis.

Table 5. CFA of experience quality.

Factor	Item	Std. Error	p-Value	Composite Reliability	Average Variance Experience
Tangible Attraction	water ex	-	-	0.940	0.757
	buildings ex	0.043	0
	flowers ex	0.048	0
	forests and mountains ex	0.046	0
	relics ex	0.044	0
Intangible Attraction	Hui ethnic group folklore ex	-	-	0.949	0.787
	Manchu ethnic group folklore ex	0.033	0
	Han folklore ex	0.031	0
	Christianity ex	0.035	0
	imperial exam ex	0.034	0
Facilitating Supply	responsible government ex	-	-	0.983	0.786
	friendly residents ex	0.037	0
	safety experience ex	0.035	0
	cleanliness ex	0.039	0
	festivals ex	0.037	0
	participation ex	0.037	0
	price ex	0.039	0
	informationalization ex	0.039	0
	art ex	0.037	0
	ecology ex	0.035	0
	transportation ex	0.041	0
	food and beverage ex	0.036	0
	hotel ex	0.038	0
	entertainment ex	0.038	0
	nightlife ex	0.039	0
	shopping ex	0.040	0

shows that the constructs are reliable and consistent with a value of composite reliability (CR) above 0.70. Values of average variance extracted (AVE) greater than 0.708 (Hair et al., 2010) and 0.50 ascertained the convergent validity as well. In

Table 6. Discriminant validity: Pearson correlation coefficient and squared root of average variance extracted (AVE) for experience quality.

	Tangible Attraction	Intangible Attraction	Facilitating Supply
Tangible Attraction	0.870
Intangible Attraction	0.811	0.887
Facilitating Supply	0.815	0.844	0.887

, the squared root of AVE of a certain construct, such as 0.870, 0.887, and 0.887, is greater than the correlation of the construct with all other constructs, showing the accepted discriminant validity of the constructs of experience quality.

4.4. Model Estimation

Hypotheses developed in the study were checked via the partial least-squares structural equation modeling (PLS-SEM) method (Hair et al., 2010; Ramayah et al., 2016), because PLS-SEM is featured with a casual-predictive technique. Subsequently, the model parameters were estimated by using SmartPLS 3.3.7. According to Hair et al. (2017), a two-stage approach was applied in the data analysis: (1) measurement model assessment and (2) structural model assessment. As for the path weighting, a maximum of 300 iterations and a stop criterion of 10-7 was used in the algorithm settings.

4.5. Reflective Measurement Model

The internal reliability, convergent validity, and discriminant validity, as the three dominating results in the reflective measurement model, were constructed. As shown in

Table 7. Assessment of reflective measurement model.

Construct	Sub-Dimension	Item	Loading	CA	Rho-A	CR	AVE
Cognition (C)	Tangible attraction cognition (TC)	water cog	0.824	0.920	0.921	0.940	0.759
		buildings cog	0.855
		flowers cog	0.872
		forests and mountains cog	0.896
		relics cog	0.908
	Intangible attraction cognition (IC)	Hui ethnic group folklore cog	0.908	0.949	0.950	0.961	0.831
		Manchu ethnic group folklore cog	0.930
		Han folklore cog	0.885
		Christianity cog	0.924
		imperial exam cog	0.909
	Facilitation cognition (FC)	responsible government cog	0.903	0.986	0.986	0.987	0.826
		transportation cog	0.869
		food and beverage cog	0.918
		hotel cog	0.903
		entertainment cog	0.926
		nightlife cog	0.900
		shopping cog	0.908
		friendly residents cog	0.929
		safety cog	0.914
		cleanliness cog	0.892
		festivals cog	0.911
		participation cog	0.922
		price cog	0.890
		informationalization cog	0.918
		art cog	0.925
		iconology cog	0.916
Experience quality (E)	Tangible attraction experience quality (TE)	water ex	0.842	0.939	0.941	0.953	0.804
		buildings ex	0.917
		flowers ex	0.908
		forests and mountains ex	0.922
		relics ex	0.890
	Intangible attraction experience quality (IE)	Hui ethnic group folklore ex	0.914	0.948	0.949	0.960	0.829
		Manchu ethnic group folklore ex	0.930
		Han folklore ex	0.902
		Christianity ex	0.907
		imperial exam ex	0.898
	Facilitation experience quality (FE)	responsible government ex	0.886	0.983	0.983	0.985	0.800
		transportation ex	0.868
		food and beverage ex	0.910
		hotel ex	0.899
		entertainment ex	0.910
		nightlife ex	0.894
		shopping ex	0.899
		friendly residents ex	0.9
		safety ex	0.905
		cleanliness ex	0.866
		festivals ex	0.896
		participation ex	0.915
		price ex	0.892
		informationalization ex	0.889
		art ex	0.884
		iconology ex	0.893
Affection (A)		distressing–relaxed	0.877	0.886	0.888	0.930	0.815
		unpleasant–pleasant	0.895
		sleepy–lively	0.935

Note: CA—Cronbach’s alpha; rho_A—Dijkstra–Henseler’s rho; CR—composite reliability; AVE—average variance extracted; cog—cognition; ex—experience quality.

, Cronbach’s alpha (CA), composite reliability (CR), and the Dijkstra–Henseler’s rho (rho-A) are all above 0.70, which indicates that the reflective constructs are all reliable and consistent. The values of loadings and average variance extracted (AVE) are greater than 0.708 [22,34,56]; thus, the convergent validity of the reflective measurement is ascertained. The heterotrait-monotrait (HTMT) ratio correlation criteria was used to assess the discriminant validity of the reflective measurement. All constructs showed a satisfactory discriminant validity with values less than the threshold of 0.90, as shown in

Table 8. Assessment of discriminant validity using heterotrait-monotrait ratio (HTMT).

Reflective Construct	A	FC	FE	IC	IE	OI	TC	TE
Affection (A)
Facilitation cognition (FC)	0.671
Facilitation experience quality (FE)	0.726	0.820
Intangible attraction cognition (IC)	0.637	0.873	0.712
Intangible attraction experience quality (IE)	0.683	0.713	0.875	0.820
Overall image (OI)	0.626	0.647	0.764	0.592	0.677
Tangible attraction cognition (TC)	0.623	0.832	0.679	0.867	0.676	0.568
Tangible attraction experience quality (TE)	0.683	0.669	0.851	0.659	0.861	0.677	0.786

.

4.6. Reflective-Formative Second-Order Constructs

The two-stage approach to PLS-SEM, which covers higher-order construct tests, was adopted in this study. Cognition and experience quality are both reflective-formative higher-order constructs (HOCs), where cognition is composed of three lower-order constructs (LOCs) including tangible attraction cognition, intangible attraction cognition, and facilitation cognition, and experience quality is composed, accordingly, of three LOCs including tangible attraction experience quality, intangible attraction experience quality, and facilitation experience quality. The redundancy analysis was used to assess the convergent validity of the HOCs, and it was found that the global item values of 0.861 and 0.853 are definitely greater than 0.70. To assess collinearity in PLS-SEM, a VIF < 5 indicates no potential collinearity problem. Further, the p-value of the outer weights of all the dimensions is found to be significant at a 0.1 level for intangible attraction cognition (IC), facilitation cognition (FC), intangible attraction experience quality (IE), and facilitation experience quality (FE), whereas it was not significant for tangible attraction cognition (TC) and tangible attraction experience quality (TE) (

Table 9. Assessment of higher-order construct.

Higher-Order Construct	Sub-Dimension	Convergent Validity	Outer Weights	Outer Variance Inflation Factor	t-Value	p-Value
Cognition (C)	Tangible attraction cognition (TC)	0.861	0.116	3.307	1.237	0.216
	Intangible attraction cognition (IC)		0.174	4.269	1.770	0.077 *
	Facilitation cognition (FC)		0.752	3.961	8.448	0.000 ***
Experience	Tangible attraction experience quality (TE)	0.853	0.104	3.588	1.289	0.197
quality (E)	Intangible attraction experience quality (IE)		0.197	4.147	2.134	0.033 *
	Facilitation experience quality (FE)		0.739	4.256	9.232	0.000 ***

* p < 0.1; *** p = 0.000.

).

4.7. Structural Model Assessment

The structural model was administered in a five-step approach. For the first step, the collinearity issue was not posed for the inner VIF values below the threshold of 0.33 (Diamantopoulos and Siguaw, 2006).

For the second step, the hypotheses were examined with a bootstrapping technique (5000 resampling). The output (

Table 10. Assessment of structural model.

Hypothesis	Relationship	Standard Beta	Standard Deviation	t-Value	p-Value	R2	Variance Inflation Factor	f2	Q2
H1	Cognition -> Overall image	0.455	0.049	9.349	0	0.471	1.700	0.232	0.463
H2	Affection -> Overall image	0.299	0.044	6.802	0		1.700	0.100
H3	Cognition -> Affection	0.237	0.048	4.925	0	0.497	2.861	0.039	0.399
H4	Cognition -> Experience quality	0.806	0.03	26.678	0	0.649	1.000	1.849	0.544
H5	Experience quality -> Affection	0.500	0.046	10.747	0		2.861	0.174

) shows that cognition (H3: β = 0.247, p = 0.00) and experience quality (H2: β = 0.500, p = 0.00) both have a positive effect on affection; cognition (H1: β = 0.806, p = 0.00) has a positive effect on experience quality; and cognition (H5, β = 0.455, p = 0.00) and affection (H4: β = 0.299, p = 0.00) both have a positive effect on overall image, with the cognition’s effect on overall image larger than that of affection.

Then, the output (Table 10) revealed that the predictors (R²) explain 49.7%, 64.9%, and 47.1% of the variance in affection, experience quality, and overall image, respectively.

For the fourth step, the effect size (f²) was assessed; Cohen’s three levels of effect size (1988) were used as the criteria, with the large level ≥ 0.35, medium level ≥ 0.15, and small level ≥ 0.02. In particular, cognition–experience quality (H1: f² = 1.849) exhibits a large effect size, experience quality–affection and cognition–overall image (H5: f² = 0.232) reveal a medium effect size, and cognition–affection (H3: f² = 0.399) and affection–overall image (H4: f² = 0.100) are found to be of a trivial effect size.

Finally, predictive relevance (Q²) was found by blindfold execution. The Q² values of 0.399, 0.463, and 0.544 were endogenous and were all found to indicate a predictive power for the model.

4.8. Assessment of Mediating Effect (H6)

The mediating analysis was checked using the approach followed by Nitzl et al. (2016) and by bootstrapping the indirect effect (

Table 11. Assessment of mediating effect.

Mediating Relationship	Indirect Effect	Standard Deviation	t-Value	p-Value
FC -> FE -> A	0.256	0.079	3.222	0.001 **
TC -> TE -> A	0.125	0.065	1.929	0.054 *
IC -> IE -> A	0.027	0.075	0.363	0.717
C -> E -> A	0.402	0.040	9.980	0.000 ***
C -> A -> OI	0.071	0.016	4.472	0.000 ***

* p < 0.1; ** p < 0.05; *** p = 0.000.

). The output supports that experience quality mediates cognition and affection (β = 0.402, p = 0.00). In particular, the facilitation experience quality is clearly observed to mediate the relationship between facilitation cognition and affection (β = 0.256, p = 0.001); similarly, the tangible attraction experience quality is confirmed to mediate tangible attraction cognition and affection (β = 0.125, p = 0.054) Although the results demonstrate the experience quality of intangible attraction to be a mediator (β = 0.027, p = 0.717) between cognition and affection, it is very trivial and not significant with a p > 0.1. Finally, affection is confirmed to significantly mediate cognition and overall image, although the effect is trivial (β = 0.071, p = 0.00).

5. Conclusions

This study aimed to empirically examine the relationships between cognition, affection, and overall image in an emerging small city in mainland China, and to test the mediating role of experience quality between cognition and affection in the overall image perception of a destination in the context of tourism destination management. The results of the study are as follows:

(1)

Through exploratory factor and validation factor analysis, the empirical study proved that the scale has good reliability and validity, which provides inspiration for the study of measuring heritage tourism motivation.

(2)

Motivation has an important role in the formation process of tourists’ image perceptions of heritage tourism places, i.e., heritage tourism motivation has an important influence on destination image perceptions, and there are differences in the influences of each dimension on destination image perceptions. Specifically, the primary concern of educationally enlightened motivated tourists is the genus of the heritage attraction of the destination, as well as the communal and public benefit value of the heritage. They generally have the characteristics of being good learners and thinkers, care for others, have unique insights and opinions about heritage, and are willing to connect with others in the process of tourism, such as by participating in “fraternal” volunteer activities. In this process, they give more emotional value to the heritage tourism place, and thus, are more satisfied with the overall image of the tourism place after the tourism experience.

(3)

There is an influential relationship between the constructs of destination image. The cognitive image positively and significantly affects the emotional image and the overall image, and the emotional image positively and significantly affects the overall image.

This paper only explores the relationship between heritage tourism motivation and destination image with Qingzhou as the research object, and the findings are inevitably limited. In the future, we can compare the mechanisms of the influence of tourist motivation on destination image through different types of heritage tourism sites. In addition, this paper adopts a quantitative research method to develop a heritage tourism motivation scale and a heritage tourism destination image scale, and subsequent research can add qualitative research content, as well as refine the research objects, classify heritage tourists, and compare the differences in the influences of destination image perception formation by groups.

6. Limitations

It seems that emerging destinations have their own rules with regard to the perception of overall image. As a further step, since this study is empirically limited, further comparisons are demanded in the future to analyze the dissimilarities between emerging destinations and matured destinations. Second, the principal purpose of this research is to affirm the mediating role of experience quality. Thus, the gap between experience quality and cognition of each of the 26 attributes was neglected, but it can be statistically counted to guide practitioners more exactly. Furthermore, the important performance analysis [79] matrix can be applied, which can help to improve the strategical management of the overall image. Finally, the sample is confined to respondents of tourists who have visited the destination, whereas the residential visitors who are important stakeholders with regard to destination are neglected; thus, this study can be expanded into a multigroup comparison [44,90] to determine the similarities and differences. As mentioned by [13], all things change with time, and systems (from a single entity to the entire planet) must adapt to their changing context or perish in some way.