Destination brand experience,brand positioning,and intention to visit: A multi-destination comparison study

Abstract

Brand experience, critical to the success of businesses, is well studied in marketing research. However, little is known about its effect on building advantages of competitive differentiation and brand positioning, especially in the tourism field. This study investigated the applicability of a well-established brand experience model from marketing research through a comparative study across multiple destinations. Factor analysis and cluster analysis were utilized to examine the effect of destination brand experience on tourist perception of destinations and regression analysis was utilized to examine the effect of four dimensions of destination brand experience on tourists' visit intention. Results showed that tourists perceived differently on all four dimensions of their brand experiences across different destinations. The sensory and affective dimensions of destination brand experience have positive effects on tourists' visit intention. This study provides marketing intelligence for destinations to create strong differentiated advantages and achieve precise brand positioning.

Keywords

Destination brand experience brand positioning multi-destination comparison study differentiated perception visit intention

Introduction

Destination branding, aiming to build up a positive and unique destination image (Crouch, 2005; Almeyda-Ibáñez et al., 2017), is critical to the development and success of tourism endeavors (Kemp et al., 2012). Through the creation of powerful destination brands, destinations can promote a differentiated product (Morgan and Pritchard, 2006), maintain a better relationship with customers (Tasci and Kozak, 2006), and position an appealing niche place in the tourism market (Morgan et al., 2002). Destination is considered a complicated tourism product where tourists as consumers experience multisenses and emotions (Govers et al., 2007). A successful destination makes a good emotional connection with tourists via positive brand impression. To interpret and predict tourist perception of destination brands, different concepts were proposed including destination brand personality (Aaker, 1997; Murphy et al., 2007), destination image (Baloglu and McCleary, 1999), brand attachment (McAlexander et al., 2002), brand trust (Yague-Guillen et al., 2003), brand love (Thomson et al., 2005), and customer-based brand equity (Boo et al., 2009). However, each of these constructs only concentrated on a specific dimension, and thus was considered incomprehensive, failing to capture the all-around experience affecting consumers from stimuli related to brands (Brakus et al., 2009). For the past few years, brand experience, a relatively new concept in marketing research, has suggested a new theoretical perspective for differentiating consumer perception of brands.

Brand experience, which refers to the internal reactions of consumers (e.g. feelings, cognition) and behavioral responses produced by stimuli related to brands, is subjective and has multiple dimensions, such as experiences on sense, affection, intellect, and behavior (Brakus et al., 2009). Brand experience instills into customer experience an overall perception formed through their interaction with the brand, and has the capacity to affect satisfaction (Chinomona, 2013) and brand loyalty (Beckman et al., 2013; Kumar and Kaushik, 2017), either directly or indirectly. As such, understanding how consumers experience brands to help formulate marketing strategies for products and services is critical. Furthermore, by integrating consumers’ complex psychological and behavioral responses in purchasing and consumption, brand experience helps better illustrate the connotation of brand and more comprehensively evaluate consumer behaviors (Kumar and Kaushik, 2017).

Although been previously applied to various fields and products (e.g. smartphone, Iqbal et al., 2020; fashion apparel industry, Joshi and Garg, 2021), brand experience is still understudied in the tourism context, despite the proven capability to comprehensively understand tourist perception of destinations and capture their diverse experience on destinations (Barnes et al., 2014). Limited existing research is mainly focused on the role of destination brand experience on tourist attitude and behavior (e.g. brand loyalty, tourist satisfaction, visit intention and recommendation; Beckman et al., 2013; Jiménez-Barreto et al., 2019b). Research is scarce regarding the influence of brand experience on tourist perception of destination brands. Therefore, this study aimed to investigate the effect of brand experience on tourist perception of destinations and their visit intention. Study results provide practical implications to guide both positioning and management of destination brands, which help destinations achieve advantages of competitive differentiation.

Literature review

Brand experience: Concept and dimensions

Concept of brand experience

Experience, defined as the subjective inner psychological feelings generated from the interaction between consumers and companies (Meyer and Schwager, 2007), is a dominating concept in marketing research, and thus has been extensively examined in its various forms including product (Hoch, 2002), service (Ngo et al., 2016), and consumption experience (Holbrook and Hirschman, 1982; Cifci, 2022). Companies carefully design their products to attract customers by providing an unforgettable experience (Wong, 2013). Experience can occur on all occasions, either directly while consumers are shopping, buying, and consuming goods or indirectly being exposed to advertisement and brand marketing. Hence, when searching and shopping for brands, consumers are influenced by product attributes, and a variety of specific stimuli related to brands, which refers to “brand experience” (Brakus et al., 2009).

Different from the aforementioned forms, the concept of brand experience is widely perceived to be related to specific interpretation. It is formed through varieties of stimuli associated with brands (Brakus et al., 2009) including slogans, mascots, and brand characters (Keller, 1987), colors to identify the brand (Gorn et al., 1997; Meyers-Levy and Peracchio, 1995), typefaces, background design elements (Mandel and Johnson, 2002), and therefore encompasses the impact that brand-related stimulants have on consumers. As such, brand experience was conceptualized by Brakus et al. (2009) as subjective and internal responses (e.g. feelings, sensations, and cognitions) and behavioral reactions from consumers (e.g. purchase intention, brand satisfaction, and brand loyalty). Such responses were triggered by stimuli related to brands (e.g. brand name, logo system, brand characteristics, brand meaning, packaging, and brand communication) that were part of a brand's design and identity, environment, packaging, and communications, which made brand experience a comprehensive experience of consumer actions, emotions, thoughts and senses (Brakus et al., 2009). Jiménez-Barreto et al. (2019b) suggested that brand experience should focus on explaining the different feelings brought to consumers by the stimuli related to the brand. Lin and Wong (2020) also held that brand experience was jointly created by consumers and brands. As such, brand experience is the overall value experience of consumers.

Dimensions of brand experience

Earlier marketing research suggested the multidimensional nature of consumer experience (Gentile et al., 2007; Schmitt, 1999), which led to the postulation that brand experience was also multifaceted. Different models, therefore, were developed to measure brand experience. Schmitt (1999) categorized customer experience into five (i.e. sense, affection, creative cognition, physical behaviors and lifestyle, and social identity) dimensions, while Gentile et al. (2007) kept the sensory, cognitive, and lifestyle dimensions of Schmitt’s (1999) model but introduced three (i.e. emotional, pragmatic, and relational) new dimensions. Thus far, research on the comprising dimensions of consumer experience is not conclusive. Among different consumer experience models, Brakus et al. (2009) suggested that four (i.e. sensory, affective, intellectual, and behavioral) dimensions were experienced by consumers in their consumption of brands. Specifically, sensory dimension referred to five physical senses including hearing, sight, touch, smell, and taste (e.g. the delicacy or the fresh air in the forest); affective dimension was comprised of perception and affection (e.g. a lovely homely feeling brought by a hotel); intellectual dimension included thought, craving for knowledge, and competence in solving problems(e.g. a provocative historical attraction); and behavioral dimension referred to factors related to behaviors (e.g. walking in the park, tattooing, or dancing).

Brakus et al.'s (2009) four dimensions of brand experience model have been widely adopted. For example, this model was validated by Nysveen et al. (2013) when examining a published scale of brand experience under the setting of a service brand. Barnes et al. (2014) used the model to test both the direct and mediating role of the four dimensions of brand experience in tourist intention to return and word-of-mouth (WOM) recommendation in the tourism context. Jiménez-Barreto et al. (2019b) attempted to extend Brakus et al.’s (2009) model by adding two other dimensions (interactive and social) in the examination of online destination brand experience, only to confirm that destination marketing organizations should still focus on the original four dimensions. Khan and Fatma (2021) also adopted and confirmed Brakus et al.’s (2009) model in their examination of the relationship between online destination brand experience and destination brand authenticity.

Brand experience in marketing research

Much research has examined the significance of brand experience in marketing practice (Chattopadhyay and Laborie, 2005; Singh and Mehraj, 2018) and its association with other variables (e.g. satisfaction, brand equity, and loyalty, Chinomona, 2013; Lin, 2015; Cleff et al., 2018). For instance, Brakus et al. (2009) defined and tested brand experience while researching different brands (e.g. Disney, Nike, American Express, Starbucks, Hilton, Microsoft, and Walmart), and found that consumer satisfaction and loyalty were positively affected by brand experience via brand personality. Cleff et al. (2013) confirmed the four dimensions of brand experience positively influence customer satisfaction and loyalty in a case study of Adidas. Prentice et al. (2019) also found that brand experience exerted significant direct and indirect influence on customer engagement in their study on airlines. In addition to the direct effect of brand experience, some scholars examined other mediating variables as well including consumer satisfaction (Terblanche and Boshoff, 2006), affective commitment (Iglesias et al., 2011), brand relationship quality (BRQ) (Francisco-Maffezzolli et al., 2014), brand love (Huang, 2017), brand trust (Huang, 2017), and consumer decision to customize products (Pallant et al., 2022). However, results are not conclusive. For example, Sahin et al. (2012) found that brand experience affected brand satisfaction and brand commitment positively, while Walter et al.’s (2013) research indicated that brand experience did not significantly affect satisfaction. Additionally, in view of the value of brand experience, a few studies had also explored factors such as interpersonal interaction on brand experience nurturing (Lin and Wong, 2020), which indicated that employee-to-customer (E2C) interaction had the moderating effect between the customer interaction and brand experience.

Previous research showed that brand experience had a complex effect on brand satisfaction and loyalty, with each comprising dimensions of brand experience having different effects (Nysveen et al., 2013, Cleff et al., 2014). As a case in point, Nysveen et al. (2013) found in the study of service organizations that only relational dimension affected both brand satisfaction and loyalty significantly and positively, while intellectual dimension affected brand satisfaction significantly and negatively and the rest of dimensions had insignificant effect on brand satisfaction and loyalty. Ong et al. (2018) also confirmed that different brand experience dimensions had varying effects on the components (i.e. willingness to pay more—WTP, WOM, and repurchase intention) of brand loyalty in their study of two successful Malaysian casual dining restaurant brands. For example, the sensory dimension of brand experience influenced individuals’ WTP and repurchase intention, but not WOM, while the affective dimension significantly impacted the WOM and repurchase intention, but not the WTP. On the other hand, the behavioral dimension encouraged individuals’ WTP and WOM, but not repurchase intention, while the intellectual dimension was closely related to all three components of brand loyalty.

These findings on the association between brand experience and consumer attitude and behavior (e.g. brand loyalty, Nysveen et al., 2013; Ong et al., 2018) show that brand experience is critical in marketing strategies. In general, consumers obtain tailored experience through the process of selection, purchasing, use, and reuse of the brand (Bennett et al., 2005), in which their needs are met and the value obtained from these experience elements will influence their attitude toward the brand (Frow and Payne, 2007) and purchasing decisions (Bennett et al., 2005). Therefore, brand experience influences the differentiation of consumer perception, and can be used to comprehensively evaluate consumer experience and to predict their attitude and behavior (Barnes et al., 2014).

Destination brand experience research

To date, only a few researches (Beckman et al., 2013; Jiménez-Barreto et al., 2019b) have investigated the influence of brand experience on tourist behavior in the tourism field. Limited existing research found that positive destination brand experience led to tourists positive attitude toward destinations (Kumar and Kaushik, 2017), including their revisit intention, WOM, loyalty, and satisfaction (Beckman et al., 2013; Barnes et al., 2014; Kumar and Kaushik, 2017). However, previous research primarily examined destination brand experience in the online domain. For instance, through navigating destination social media using a scenario experiment approach, Zhang et al. (2018) inspected emotional experiences of social media users, and found that their emotional experience was significantly affected by online platform experience which was mediated by destination engagement intention.

Moreover, the online destination brand experience (sensory and cognitive dimensions) also had a mediating effect in the relationship with user attitudes, perceived website quality, recommendation, and visit intention, as well as the sensory-to-cognitive experience's directionality (Jiménez-Barreto et al., 2019a). In a follow-up study, Jiménez-Barreto et al. (2020) examined several online official destination platforms (destination website, Instagram, Facebook, YouTube, and Twitter) and found that the relationships between online destination brand experience and behavioral tendencies toward destinations of users were mediated by online destination brand credibility perceived by users. The findings showed that online destination brand experience was assigned a greater importance by users who had not visited destinations to shape their behavioral tendencies; and for users who had visited destinations, the relationship between online destination brand credibility and behavioral tendencies had a higher intensity. Lately, Khan and Fatma (2021) examined online destination brand experience as associated with destination brand authenticity, WOM, and affective commitment through survey research and found that the effect of online destination brand experience on affective commitment was fully mediated by destination brand authenticity, while the effect on WOM was partially mediated by destination brand authenticity.

These studies on online destinations (Nysveen, et al., 2013; Jiménez-Barreto et al., 2019a) did not examine all dimensions from Brakus et al.’s (2009) brand experience model. Furthermore, destination brand experience research is still at the inception stage, with only a handful of studies examined in the offline domain.

Research questions

Although destination brand experience is critical for destination marketing (Jiménez-Barreto, et al., 2020), there are still several research gaps to be addressed including:

(1) Lack of understanding in multi-destination decision-making context. Previous research only focused on the effect of destination brand experience on tourist behavior and attitude for a specific destination (Jiménez-Barreto, et al., 2020), while in reality, tourists can choose from multiple destinations with similar offerings, so their decision-making process may differ from when they only have no other destination options. To bridge this gap, this study validates the destination brand experience from the multi-destination comparative study perspective, which better reflects the value of destination brand experience.

(2) Lack of horizontal perspective of competition between destinations. Despite the increasing interest in destination brand experience, most previous research concentrated on destination brand experience as associated with customer attitude and behavior (Beckman et al., 2013; Jiménez-Barreto et al., 2020; Khan and Fatma, 2021), while its effect on the competition among destinations received limited attention. Generally, to be successfully unique from competitors, destination brands have to convey promises of an unforgettable destination experience (Hudson and Ritchie, 2009). This requires an identification of core characteristics of destination brand experience to build competitive advantages over competitors. Nevertheless, brand experience, as an effective means of differentiation, has rarely been explored in marketing research. Whereas this study obtains the core characteristics of destination brand experience when performing a multi-destination comparison analysis.

(3) The role of brand experience in destination brand positioning has been overlooked. Pulling from the literature, tourist experience in each dimension of destination brand experience constituted their overall destination experience (Jiménez-Barreto et al., 2020) and helped differentiate the destination from other competitors (Ong et al., 2018). It is postulated that evaluations of destinations’ performance on the brand experience dimensions can help destination positioning, which awaits for further research exploration.

Therefore, this study is conducted to address these research gaps through the application of destination brand experience model, using a multi-destination comparison study. Specifically, this study aims to verify the brand experience's four dimensions in the destination context from the multi-destination comparative perspective, and to capture core characteristics of destination brand experience which can help destination brand positioning. In addition, as consumer brand experience positively affects their overall attitudes, beliefs, and behaviors in tourism settings (Lee and Gretzel, 2012; Khan and Fatma, 2021), it is postulated that positive destination brand experience is likely to evoke positive tourist response toward the destination, which leads to an increased visit intention. Hence, this study also investigates empirically the unique effects of each brand experience dimension on tourists’ visit intention. As such, three research questions are proposed:

Research question 1: Will the four dimensions of brand experience model be applicable to reflect the brand experience of destinations when compared with other destinations with similar offerings?

Research question 2: Can the destination brand experience help identify core characteristics of destinations in their efforts toward competitive differentiation and brand positioning?

Research question 3: Will the four dimensions of destination brand experience (sensory, affective, intellectual, and behavioral) help predict tourists’ visit intention?

Methodology

The List of Excellent Tourism Cities of China (published by the National Tourism Administration of China, 2017) included the cities that received the most tourists and are most competitive in the Chinese tourism market. These destinations are popular among tourists for having their own unique characteristics, but they competed with each other in attracting tourists in the meanwhile. Based on the List and the geographical distribution, we selected the most representative destination cities for each of all 27 provinces and all four government-controlled municipalities in mainland China, with a total of 31 destinations.

Measurement scales and questionnaire

A questionnaire was designed to collect information on tourists’ demographic characteristics, destination brand experience, and their visit intention. Respondents were asked to evaluate all 31 destinations included in this study other than their birthplaces, current city of residence, or those they have visited already, on destination brand experience. Demographic data included gender, age, education, and monthly income. To assess brand experience, a 12-item scale (Table 2) was developed based on previous research (Barnes et al., 2014), which comprised three items for each of the four dimensions of brand experience (sensory, affective, intellectual, and behavioral). A 7-point Likert scale was employed to measure the scale of brand experience model (from 1 = “strongly disagree” to 7 = “strongly agree”). Tourists’ visit intention was measured using one single item of “visit intention” based on Ng et al.’s (2007) study on tourist destination choice, employing a 7-point Likert scale (from 1= “strongly dislike to 7 = “strongly like”).

Data collection and analysis

This survey was distributed online to residents across mainland China from September to November in 2019 through the website of the Wenjuanxing (wjx.cn; a common survey platform in China for social science). A remuneration of 20 CNY was provided for each respondent. Of the 1000 distributed questionnaires, 781 were collected with 705 valid responses (70.5% response rate, Table 1). Respondents’ demographic characteristics were delineated using descriptive statistics.

Table 1.

Respondents’ demographic characteristics.

Characteristics		Frequency	Percentage (%)
Gender	Male	326	46.2
Gender	Female	379	53.8
Age	Below 18	21	3.0
	18–25	54	7.7
	26–35	241	34.2
	36–45	207	29.4
	46–60	116	16.5
	Above 60	66	9.4
Education	High school or less	287	40.7

	Bachelor’s degree	306	43.4
	Postgraduate degree and above	112	15.9
Monthly income	Below 5000 CNY	154	21.8
	5001–8000 CNY	290	41.2
	8001–17,000 CNY	189	26.8
	17,000 CNY above	72	10.2

Dimension means for each brand experience dimensions were calculated. The internal reliability of each dimension of brand experience was assessed using Cronbach's alpha. The four-dimensional structure of brand experience was examined exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Sample destinations were classified using cluster analysis based on brand experience, and the effect of the four destination brand experience dimensions on potential tourists’ visit intention was analyzed through regression analysis.

Results

Most respondents were female (53.8%, Table 1), between the ages of 26 and 45 years (63.5%), with at least a bachelor's degree (59.3%), and a monthly income above 5000 CNY (78.2%). Cronbach's α showed high internal reliability among items within each brand experience dimension (sensory, α = 0.907; affective, α = 0.907; intellectual, α = 0.907; behavioral, α = 0.909).

EFA and CFA

The sample was divided into two parts (even vs. odd numbers). EFA and CFA were utilized to confirm the factor structure and test the validity of this scale. Results from EFA analysis showed that the sample data was appropriate for a factor analysis (Kaiser–-Meyer–Olkin = 0.871, and Bartlett's test of sphericity was significant, p = 0.000).

As the scale structure of the destination brand experience, four common factors have been extracted from 12 items, with a variance contribution of 20.5–21.6% (Table 2). The cumulative variance contribution of the four common factors was 84.5%. The factor loadings of the items were all above 0.8, with no cross-factor loading of greater than 0.3.

Table 2.

Results of exploratory factor analysis (EFA).

Common factors	Items	Variance contribution	Factor loadings
Sensory	Q1 (Destination name) makes a strong impression on my senses, visually and in other ways	21.6%	0.829
	Q2 I find this (destination name) interesting in a sensory way		0.863
	Q3^a (Destination name) does not appeal to my senses		0.869
Affective	Q4 (Destination name) induces feelings and sentiments	21.4%	0.837
	Q5^a I do not have strong emotions for (destination name)		0.865
	Q6 (Destination name) is an emotional area		0.881
Behavioral	Q7 I engage in physical activities and behaviors when I am on (destination name)	21.0%	0.842
	Q8 (Destination name) gives me bodily experiences		0.847
	Q9^a (Destination name) is not activity oriented		0.864
Intellectual	Q10 I engage in a lot of thinking when I am on (destination name)	20.5%	0.869
	Q11^# (Destination name) does not make me think		0.873
	Q12 (Destination name) stimulates my curiosity and problem solving		0.884

Note: ^aIndicates reverse-coded items. Destination names in the questionnaire are called the corresponding destination names.

Results from CFA showed that the structure model composed of the four experience dimensions on sense, affection, intellect, and behavior was appropriate to the data. First, as for the goodness of fit, χ²/df = 2.352, the RMSEA value was 0.063, satisfying the adaptation requirement of less than 0.08. Moreover, GFI (0.955), NFI (0.977), and CFI (0.982) all reached the criterion of greater than 0.9. The factor loadings of each item were between 0.833 and 0.933 (Table 3), all of which were greater than 0.8 with statistical significance. The composite reliability (CR) of the four variables was 0.883–0.939, greater than the criterion of 0.6, and the average variance extracted (AVE) was 0.715–0.837, greater than the criterion of 0.5. These results showed that the model fulfilled the convergent validity requirement. Furthermore, the square root of the AVE for each of the four latent variables of sensory, affective, intellectual, and behavioral was greater than the correlation coefficient of any other variable (Table 4), which indicated that the scale had a good discrimination validity. Results from CFA answered Research Question 1, which showed that the destination brand experience could be measured by the four-dimensional structure of experiences on sense, affection, intellect, and behavior.

Table 3.

Results of confirmatory factor analysis (CFA).

Variable	Items	Factor loadings	Composite reliability (CR)	Average variance extracted (AVE）
Sensory	Q1	0.868	0.918	0.788
	Q2	0.860
	Q3	0.933
Affective	Q4	0.902	0.909	0.769
	Q5	0.865
	Q6	0.864
Behavioral	Q7	0.930	0.939	0.837
	Q8	0.895
	Q9	0.920
Intellectual	Q10	0.843	0.883	0.715
	Q11	0.860
	Q12	0.833

Table 4.

Mean standard correlation and square root of average variance extracted (AVE).

Variable	Mean	Std.	1	2	3	4
Sensory	5.446	1.079	0.888
Affective	4.892	1.286	0.347**	0.877
Behavioral	5.029	1.260	0.477**	0.505**	0.915
Intellectual	4.929	1.255	0.244**	0.502**	0.364**	0.845

Note: The diagonal of the table is square root of AVE; Off-diagonal data are the correlation coefficients between potential variables.

*Denotes p<0.05, **Denotes p<0.01.

Cluster analysis of destinations

Cluster analysis was used to classify all 31 destinations, based on the scores of the sample destinations in the four dimensions of brand experience and the “link between groups” method. As a result of cluster analysis, this study estimated the best classification number according to the above aggregation coefficient. The aggregation coefficient curve changed from steep to flat when the sample was divided into seven categories (Figure 1). These seven clusters were used to analyze the destination characteristics (Table 5). With the help of corresponding analysis, the features of 31 destinations in four dimensions were displayed in a two-dimensional plot and the corresponding relationships were analyzed according to their distance. Results showed that the singular value and the inertia of Dimension 1 were 0.047 and 0.005, respectively, which explained 61.9% of the differences of all dimension types; the singular value and the inertia of dimension 2 were 0.052 and 0.003, respectively, which explained 30.7% of the differences of all dimension types. In total, the first two dimensions accounted for 92.7% of all differences. Thus, the two-dimensional distribution map can be used to express the relationship between variables. Figure 2 revealed five distinctive groups (Cluster 5 and Cluster 6 were combined into one located in the central part of the figure and Cluster 4 and Cluster 7 were grouped into one located in the upper-right). The classification of seven clusters helps to better describe the sample destination types.

Figure 1.

Plot of clustering coefficient against the increasing number of clusters.

Figure 2.

Results of corresponding analysis.

Table 5.

Results of one-sample t-test.

Cluster	Destination	Dimension	Mean difference	t-value	p
Cluster 1: High Sensory	Chengde, Zhangjiajie, Huangshan, Guilin, Chongqing, and Urumchi	Sensory	0.661	169.280	.000
		Affective	−0.593	77.769	.000
		Behavioral	−0.403	114.636	.000
		Intellectual	−0.798	59.973	.000
Cluster 2: High Affective	Lijiang, Yan’an, and Zunyi	Sensory	−0.649	61.025	.000
		Affective	0.804	75.098	.000
		Behavioral	−0.216	104.286	.000
		Intellectual	−0.253	60.852	.000
Cluster 3: High Sensory & High Behavioral	Hohhot, Harbin, Changchun, Sanya, Qingdao, and Dalian	Sensory	0.743	54,704	.000
		Affective	−0.546	64.736	.000
		Behavioral	0.691	129.383	.000
		Intellectual	−0.873	98.865	.000
Cluster 4: High Intellectual & High Sensory	Dunhuang, Dujiangyan, Yichang, and Jingdezhen	Sensory	0.115	79.204	.000
		Affective	−0.705	34.004	.000
		Behavioral	−0.85	80.864	.000
		Intellectual	0.525	50.548	.000
Cluster 5: High Balanced	Hangzhou, Beijing, Shanghai, Lhasa, Nanjing, and Xiamen	Sensory	0.738	151.970	.000
		Affective	0.701	60.237	.000
		Behavioral	0.463	57.670	.000
		Intellectual	0.721	65.719	.000
Cluster 6: Moderately Balanced	Tianjin, Guangzhou, Zhengzhou, and Taiyuan	Sensory	0.335	41.522	.000
		Affective	0.11	49.234	.000
		Behavioral	0.15	33.078	.000
		Intellectual	0.063	39.764	.000
Cluster 7: Low Balanced	Xining and Yinchuan	Sensory	−0.668	98.111	.006
		Affective	−1.218	51.533	.012
		Behavioral	−1.463	60.333	.011
		Intellectual	−0.163	82.000	.008

Note: The data in columns of mean difference, t-value, and p were the results of comparison between the corresponding clusters and the mean value of 31 destinations.

This study further analyzed the characteristics of seven clusters. (1) One-sample t-test was utilized to compare the differences between the scores of each cluster and all samples in the four dimensions of brand experience. The mean score of each dimension of sensory, affective, intellectual, and behavioral for all destinations is 5.45, 4.92, 5.02, and 4.94, respectively. (2) Paired-samples t-test was employed to analyze whether the performances of each brand experience dimension of each destination are significantly different (Appendix 1). (3) Based on the comparison result between the means of clusters and all samples, and between each cluster and four dimensions, each cluster is named and their characteristics are described as follows:

Cluster 1 includes Chengde, Zhangjiajie, Huangshan, Guilin, Chongqing, and Urumqi. The destinations in Cluster 1 have outstanding performances in the sensory dimension and are mostly tourist attractions with distinct landscapes. Hence, these destinations are named “High Sensory”.

Cluster 2 includes Lijiang, Yan’an, and Zunyi, among which Yan’an and Zunyi are destinations with a strong feature of historical emotions. For instance, Lijiang is known as “the city of petty bourgeoisie, the capital of love affairs”, where many white-collar workers yearn for their emotional sustenance. Thus, they are named “High Affective”.

Cluster 3 includes Hohhot, Harbin, Changchun, Sanya, Qingdao, and Dalian. The destinations in Cluster 3 have distinct climate-related features. For example, Hohhot is in the Mongolian Grassland, Harbin and Changchun are in the Northeast with abundant snow, and Sanya, Qingdao, and Dalian features beautiful coastlines. Therefore, this kind of destination perform prominently in senses for their abilities to develop tourist activities based on unique natural resources, and are thus named “High Sensory & High Behavioral”.

Cluster 4 includes Dunhuang, Dujiangyan, Yichang, and Jingdezhen. Dunhuang is famous for its religious culture with a grotto cluster boasting frescoes in largest quantity. Dujiangyan is a famous ancient irrigation and flood control project. The Three Gorges Dam in Yichang is the largest hydroelectric project in the world. Jingdezhen is known for its porcelain. Therefore, the four destinations not only have strong sensory characteristics, but also showcase the capability of human beings to subdue nature, and are thus named “High Intellectual & High Sensory”.

Cluster 5 includes Hangzhou, Beijing, Shanghai, Lhasa, Nanjing, and Xiamen. These destinations are the top ranked tourism destination cities with rich history, distinguished local culture, and well-established tourism industry, and are named “High Balanced”.

Cluster 6 includes Tianjin, Guangzhou, Zhengzhou, and Taiyuan. Destinations in this cluster have similar characteristics with Cluster 5, and show a balanced but lower performance in four dimensions, and are thus named “Moderately Balanced”.

Cluster 7 includes Xining and Yinchuan. They are more remotely located with inconvenient transportation and underdeveloped tourism industry. Because of the low awareness of these destinations among tourists and the low scores in four dimensions, they are named “Low Balanced”.

Thus, regarding research Question 2, results showed that destinations can be divided into different groups based on four dimensions of brand experience. Destinations exhibit differentiated performances on brand experience dimensions, which represent competitive advantages over competitors. Therefore, these are core characteristics of destinations to help them make precision positioning.

Regression analysis

This study further analyzed the effect of four dimensions of destination brand experience (sensory, affective, intellectual, and behavioral) on potential tourists’ visit intention while exploring their predictive ability in tourist behavior. Following the principle of stepwise regression, the four independent (sensory, affective, intellectual, and behavioral) variables were gradually included in the regression analysis process, and the order in which the variables entered the variance was determined according to the incremental contribution of R². Otherwise, variables that did not have the ability were excluded from the model. The regression results are shown in Table 6.

Table 6.

Results of regression analysis of 31 destinations.

Model	Model 1	Model 2	Model 3	Model 4
Sensory	0.612** (4.168)	0.579** (5.405)	0.708** (4.795)	0.581** (5.284)
Affective		0.553** (5.167)	0.646** (5.005)	0.544** (4.307)
Behavioral			−0.211 (−1.257)
Intellectual				0.020 (0.156)
F	17.373**	29.732**	20.759**	19.139**
F change		26.697**	1.580	0.024
R²	0.375	0.680	0.698	0.680
△R²		0.305	0.018	0.000

Note: () is t statistic; VIF of two independent variables in Model 2 was 1.004, means no multicollinearity.

*Denotes p<0.05, **Denotes p<0.01;

In the four model equations, Sensory and Affective of destination brand experience always significantly affected tourists’ visit intention. However, in Model 3 and Model 4, after entering Behavioral and Intellectual into the equation, F change didn’t change significantly, the increase of △R² was very small, and the significance of Behavioral and Intellectual didn’t have a significant effect. Therefore, Behavioral and Intellectual shouldn’t be entered into the final equation. The reason is that, as for Behavioral, the current travel mode of tourists in mainland China is still dominated by sightseeing, and so fewer activities offered by destinations are not the main factors pursued by potential tourists. And for Intellectual, it can be explained by “cultural homogeneity”. Many cities in mainland China have a long history over thousands of years, which is why culture may not be the key factor in attracting potential tourists.

Results showed that the Sensory and Affective dimensions positively affected the destination brand experience, but not the behavioral and intellectual dimensions. Therefore, only the two variables of Sensory and Affective were kept in Model 2. That is to say, the two dimensions of destination brand experience on sense and affection are helpful to predict the potential tourists’ visit intention. Research question 3 has been partially explicated using the methodology described above.

Discussion and conclusions

Discussions and implications

Brand experience provides a breakthrough point to study the brand strategy of competitive differentiation for destinations. Through the comparison of multiple destinations, this study tests the applicability of brand experience in tourists differentiated perception for destination brands and examines the predictive ability of destination brand experience on tourists’ visit intention.

Overall, results show that Brakus et al.’s (2009) brand experience model is applicable to the tourism destination context and suitable to measure destination brand experience toward a comprehensive understanding of tourist experience. This study thus contributed to the study of destination brands. Also, as the brand experiences of different destinations may be perceived differently by tourists, destinations can be categorized into different types based on different characteristics/performances in the four dimensions of brand experience. These characteristics are at the core of customer perception toward a destination. The destination brand experience model helps evaluate tourist destination brand perception more comprehensively, and captures the differentiated advantage of destination brands. Therefore, identifying these core characteristics is essential for destination marketing to maintain competitive advantages over competitors, and for informing destination marketers to formulate positioning strategies accordingly. Although several previous studies explored brand experience in the tourism destination context, this study explores the differentiation of destinations based on Brakus et al.’s (2009) brand experience model for the first time, which sheds light on the competitive differentiation of destinations and precision positioning of destination brands.

In addition, only two brand experience dimensions (sensory and affective) positively affected tourists’ visit intention. That's because different types of destinations provide different experiences for tourists, and not every brand experience dimension is able to affect the tourist experience for each destination in the same way (Kumar and Kaushik, 2017). Husain et al. (2022) further explained that sensory experience (e.g. visual and tactile stimuli) tends to more easily elicit more effective consumer reactions, and emotionally charged experiences can positively impact consumer shopping. Such findings were consistent with previous research (Bigné et al., 2005; Phillips and Baumgartner, 2002; Lv, et al., 2020). From the study obtained by Barnes et al. (2014), the significance of sensory experience dimension was emphasized, and the sensory experience dimension had more significant effect than affective experience dimension from their three studies in the regions of Lolland-Falster of Denmark, the city of Malmö in Sweden and Lund in Sweden. Similarly, Kumar and Kaushik (2017) also found that two brand experience dimensions (sensory and affective) had the greatest impact on visitors, while the intellectual dimension was insignificant. This could be attributable to cultural homogeneity, as participants included in this study were residents from mainland China who were familiar with Chinese culture and have been to other domestic destinations with rich cultural resources, and so are not particularly drawn to cultural factors. The finding that the behavioral experience dimension is also not significant could be related to the fact that most Chinese tourists seek sightseeing instead of activity participation when traveling on site (China Tourism Academy, 2015). Therefore, the behavioral experience doesn’t significantly affect tourists’ visit intention. In sum, this study further clarified that sensory and affective experience dimensions were powerful predictors of tourists’ behavioral tendency in the four dimensions of brand experience.

Managerial implications

This study contributes to destination marketing using a multi-destination comparison study, in which Brakus et al.’s (2009) brand experience model is applied to the tourism destination context. Results showed that sensory and affective brand experience dimensions were core characteristics of destination brand experience, and that the four dimensions of brand experience model can be used to measure destination brand experience. Results also showed that tourists perceived destinations differently based on their brand experiences, which helped cluster destinations into seven types. In this sense, destination brand experience can be used to comprehensively evaluate the tourists’ destination brand perception.

As results show that destinations can be categorized into different types based on brand experience, destination brand marketing should shift from focusing on certain single brand items (e.g. brand personality, brand trust, or brand love) to the whole destination brand experience that reflects the characteristics of destination brand comprehensively, and identifies destinations’ differentiated advantages. Doing so helps destinations distinguish themselves from competitors by providing tourists with a unique brand experience. Importantly, destination brand marketing should pay closer attention to the four dimensions of brand experience, especially the sensory and affective dimensions, to measure and monitor tourist perception of a destination brand, in order to effectively position the brand.

These two dimensions could serve as an effective predictor of tourists’ visit intention, As such, destinations should focus on these two experience dimensions (e.g. providing sensory pleasure and emotional needs for tourists) to design a distinct and positive destination experience and to establish differentiated brand image. Such destination brand identity not only resonates with tourists, but also highlights destinations’ uniqueness when compared with competitors.

Limitations and future study

This study is not without limitations by all means. Even though the validity and reliability of Brakus et al.’s (2009) brand experience model have been confirmed in the tourism destination context in this study, for greater generalizability, future research may consider examining this model at destinations in other countries or regions. Although sensory and affective experience dimensions were found significantly impact tourists’ visit intention, it is worthwhile for future research to explore other factors that may moderate such effects. In addition, it is postulated that international tourists may perceive differently than domestic tourists, especially on how much they value Chinese tourism cultural resources and their willingness to participate in tourism activities, which may make a difference in results on the intellectual and behavioral dimensions and their associations with tourists’ visit intention. Therefore, it will be interesting to further explore the effect of intellectual and behavioral experience dimensions among international tourists in future research.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by the youth project of Philosophy and Social Sciences, Tianjin (TJGLQN18-012).

ORCID iD

Shuang Lin

Appendix 1. Differences between the four dimensions of each destination.

Cluster	Destination	Mean paired differences
Cluster	Destination	Sensory—Affective	Sensory—Behavioral	Sensory—Intellectual	Affective—Behavioral	Affective—Intellectual	Behavioral—Intellectual
Cluster 1	Chengde	1.364***	1.116***	1.589***	−0.249*	0.225*	0.474***
	Zhangjiajie	1.268***	1.014***	1.478***	−0.254	0.210	0.464***
	Huangshan	1.144***	1.079***	1.417***	−0.065	0.274*	0.339***
	Guilin	1.284***	1.115***	1.467***	−0.169	0.182	0.351***
	Chongqing	1.229***	1.026***	1.309***	−0.203	0.080	0.283**
	Urumchi	1.270***	1.085***	1.529***	−0.185	0.259**	0.444***
Cluster 2	Lijiang	−1.480***	−0.327**	−0.128	1.153***	1.352***	0.199*
	Yan'an	−1.420***	−0.487***	−0.501***	0.933***	0.919***	−0.013
	Zunyi	−1.457***	−0.475***	−0.556***	0.982***	0.901***	−0.081
Cluster 3	Hohhot	1.489***	0.186*	1.698***	−1.302***	0.230	1.512***
	Harbin	1.334***	0.019	1.755***	−1.315***	0.422***	1.736***
	Changchun	1.093***	−0.234	1.328***	−1.327***	0.235*	1.562***
	Sanya	1.232***	0.268***	1.894***	−0.964***	0.662***	1.626***
	Tsingtao	1.202***	−0.006	1.422***	−1.208***	0.220	1.428***
	Dalian	1.264***	−0.044	1.468***	−1.307***	0.205	1.512***
Cluster 4	Dunhuang	0.679***	1.256***	−0.506***	0.577***	−1.185***	−1.761***
	Dujiangyan	0.666***	0.781***	−0.207	0.115	−0.873***	−0.988***
	Yichang	0.947***	0.957***	−0.424***	0.010	−1.371***	−1.382***
	Jingdezhen	0.988***	0.872***	−0.485***	−0.116	−1.473***	−1.357***
Cluster 5	Hangzhou	0.092	0.465***	0.201**	0.373***	0.109	−0.264**
	Beijing	0.062	0.195	−0.120	0.133	−0.182*	−0.315***
	Shanghai	−0.099	−0.024	−0.064	0.075	0.035	−0.040
	Lhasa	−0.259***	0.765***	−0.012	1.024***	0.247**	−0.777***
	Nanjing	0.244***	0.036	−0.238*	−0.208*	−0.482***	−0.274*
	Xiamen	0.155*	0.189	0.333**	0.034	0.179	0.144
Cluster 6	Tianjin	0.209*	0.271**	0.346***	0.062	0.136	0.074
	Guangzhou	0.329**	0.143	0.423***	−0.185	0.095	0.280**
	Zhengzhou	0.203	0.148	−0.056	−0.055	−0.259*	−0.204*
	Taiyuan	0.159	0.276*	0.381***	0.117	0.222	0.105
Cluster 7	Xining	0.513***	0.775***	−0.404**	0.263*	−0.917***	−1.179***
Cluster 7	Yinchuan	0.580***	0.809***	−0.611***	0.229	−1.192***	−1.420***

Note: *p < 0.05 (two-tailed); **p < 0.01 (two-tailed); ***p < 0.001 (two-tailed).

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