Similarity-Attraction Cluster of Outbound Chinese Tourists: Who Belongs There?

Abstract

China has become the number one source market for tourists. This article seeks to understand whether cultural/lifestyle similarity is an important pull factor for Chinese tourists when selecting a destination. Specifically, 205 Chinese tourists were surveyed about their destination choices in relation to the seven most visited outbound destinations. The results from a latent class regression analysis found a similarity-driven segment to exist for all seven destinations, with segment sizes ranging from 22% to 62% of the sample. These results suggest that a substantial segment of Chinese tourists are motivated by perceived cultural/lifestyle similarity. Generally, those with high ethnocentrism, high uncertainty avoidance, low novelty seeking or less travel experience are more likely to belong to the similarity-driven segment. Further research is needed to examine the size of this segment in larger, more inclusive cities of the Chinese population, as the current study only concentrated on tourists from three major cities.

Keywords

Introduction

The number of outbound trips made by Chinese tourists reached 149.72 million in 2018, up 14.7% from the previous year, and they spent $130 billion overseas in 2018, up 13% from the previous year (C. Xu, 2019). This number has made China the main source for outbound tourists (World Tourism Organization, 2019), suggesting huge potential for Chinese outbound destinations. However, Chinese tourists visited relatively few destinations and their trips were taken by less than 10% of China’s population. As can be seen in Table 1, most countries in the 2010 top 10 destination list remained in the top 10 in 2017, with the exception of two countries (Malaysia and Vietnam). This raises a question as to why alternative destinations have not attracted more Chinese tourists. The answer to this question is particularly important for newly granted ADS (approved destination status) destinations attempting to attract Chinese tourists.

Table 1

China Tourist Departures: 10 Most Popular Destinations

Most Popular Destinations	2010 Ranking (China National Tourism Administration, 2011)	2017 Ranking (Nielsen, 2017)
Hong Kong	1	1
Macao	2	3
Japan	3	2
South Korea	4	5
Taiwan	5	8
Vietnam	6	—
Malaysia^a	7	—
United States	8	6
Thailand	9	4
Singapore	10	7
Australia	—	9
France	—	10

The data of Malaysia include visitors from Mainland China + Hong Kong and Macau, extracted from CNTA (China National Tourism Administration).

Despite the potential for Chinese tourism, relatively few studies have focused on the destination choice drivers for Chinese outbound travellers. While potential push (e.g., values, motivations and personality) and pull factors (e.g., destination specific attributes) have been identified (see, Keating & Kriz, 2008; Li & Lu, 2016, for a review), recent articles have called for more research into the impact cultural differences have on destination choice, especially with regard to the Chinese outbound market (e.g., Keating & Kriz, 2008; C. Li et al., 2017; Su et al., 2018; Wu & Pearce, 2014; Yang & Wong, 2012). To date, research into the impact of cultural differences on travel choices has mainly focused on country-level factors, most often using long-standing published scores, such as Hofstede’s (1980) cultural dimensions (e.g., Bi & Lehto, 2018; Jackson, 2001; Ng et al., 2007; Yang & Wong, 2012). There is a need to move beyond published country-level indexes to include cognitive measures, as suggested by Shenkar (2012). The current article does this by measuring Chinese tourists’ perceptions of cultural/lifestyle similarity with a variety of important destinations to identify whether there is a segment of Chinese tourists who prefer a level of cultural similarity when they choose a destination.

Tourist segmentation is an important business strategy, as it acknowledges that tourists differ in their travel needs (Frochot & Morrison, 2000). It facilitates the identification of smaller homogeneous markets within a large heterogeneous single source market (Chen & Lin, 2012). Researchers have examined tourism segments in China, on the basis of value and satisfaction (Kau & Lim, 2005), cultural motives (Chen & Huang, 2018), cost, weather, fame and branded shopping (Li et al., 2017), travel motives to volatile destinations (Wen & Huang, 2019), and destination familiarity (Chen & Lin, 2012). This evidence suggests that cultural/lifestyle similarity may be important to some Chinese tourists. For instance, Kau and Lim (2005) found a family/relaxation segment that was dissatisfied with the relatively low availability of Chinese language communication in Singapore. Chen and Lin (2012) found that the segment with highest perceived familiarity with a destination also demonstrated highest travel intention to that destination. This suggests that there may be a significant similarity-driven segment of Chinese tourists.

The existence of a potential similarity-driven segment of Chinese tourist is also suggested by theory. The Chinese are deeply rooted in Confucianism values (Fam et al., 2009; Fam & Waller, 2003; Yang, 2012), where maintaining harmony with their travel group and the host community may be especially important. Travel destinations with a similar culture and lifestyle are likely to provide an environment in which these values can be expressed more easily, without a “loss of face” to the individual and/or China’s international image. Indeed, Hoare et al. (2011) reported that Chinese tourists in international destinations felt that they were representing China’s international image/face.

Confucianism values have been found to influence Chinese travel decisions, especially decisions in the packaged tour context. For instance, Kwek and Lee (2010) found that for Chinese corporate packaged tours, travel decisions were made by top managers, group rules were adhered to, and dining menus were lavish to show sincerity in maintaining close relationships, reflecting the Confucianism values of respect for authority, conformity and guanxi for the ultimate goal of achieving harmony. Pearce et al. (2013) reported that Chinese tourists favor packaged tours for their strong in-group interaction and affiliation with other Chinese people, where responsibility can be shown in the order of family, relatives, other associated people, and then any other people. This link between Confucianism and Chinese tourists’ behavior suggests that packaged tours may offer a way to buffer cultural distance.

Chinese tourists are also likely to find it easier to uphold the practices of Confucianism values when travelling to destinations that have a similar culture and lifestyle. For example, ordering dishes that carry symbolically prosperous meanings, to indicate respect and maintain guanxi with travel companions, tends to be more challenging in destinations with a very different food culture. Destinations that have a similar culture and lifestyle are likely to have similar norms, which should make it easier to act appropriately in a given situation.

In sum, Chinese tourists who embrace Confucianism values are likely to value cultural and lifestyle similarity. Thus, we expect that at least some Chinese tourists’ destination decisions will be driven by similarity rather than novelty. In fact, Chinese traditional cultural values discourage Chinese tourists from visiting risky and unusual destinations (Xu et al., 2008).

Tourism researchers have long argued that destination choice is influenced by cultural distance, also inversely termed “cultural similarity”; however, most of these studies were conducted outside China (e.g., Ahn & McKercher, 2015; Ng et al., 2007) or by using country-level secondary data (e.g., Jackson, 2001; Yang et al., 2016). A review of Chinese tourist segmentation studies, and influence of Confucianism value on Chinese tourists’ behavior, seems to indicate that a similarity-driven segment is likely to exist in the Chinese context. The current study empirically tests this notion using individual-level perception data collected from China residents who intend to travel overseas in the next 2 years or have travelled internationally in the past 5 years.

Literature Review

A large body of research across a range of disciplines suggests that perceived similarity influences people’s behavior. Several theories have been proposed to account for this phenomenon. The similarity-attraction hypothesis suggests that people prefer “similar” others (Byrne & Nelson, 1965). In line with this, Rokeach et al.’s (1960) belief congruence theory suggests that out-group prejudice has more to do with perceived belief dissimilarity than group membership (e.g., ethnicity). Empirical support for this was found, with similar out-groups being liked more than dissimilar out-groups (Diehl, 1988; Moghaddam & Stringer, 1987). Across a range of disciplines, the subject of similarity or congruity examined has included values, religion, group affiliation, skills, physical attributes, age, language, occupation, social class, nationality, ethnicity, and residential location (Bochner, 2003).

A growing body of tourism research also suggests that similarity–dissimilarity is an important factor in destination choice. Most of this research has focused on whether cultural similarity between a tourist’s home country and an international destination increases the likelihood of travel to that destination (e.g., Abooali & Mohamed, 2012; Bi & Lehto, 2018; Ng et al., 2007). This research has produced somewhat mixed results. For instance, Ng et al. (2007) found cultural distance to be negatively related to Australian’s travel intentions, across a range of distance measures, whereas Bi and Lehto (2018) found an inverted U-curve relationship between cultural distance and destination choice for Chinese tourists. Bi and Lehto (2018) suggested that cultural distance may be a “double-edged sword.” Based on Plog’s (1974) novelty–familiarity framework, they suggested that most tourists are located at the midpoint desiring a balance between familiarity and novelty, with the spectrum approximating a normal distribution. However, it is possible that the overall regression coefficients mask the existence of more homogeneous segments along this continuum.

The existence of similarity-driven segments has been suggested in the literature. One of the earliest tourist typologies (Cohen, 1972), classified tourists into four groups, based on a novelty and familiarity continuum. This typology suggested that the “organized mass tourist” comprised those who prefer to explore the host destination within the bubble of their familiar environment, indicating that there are people who prefer more familiarity/similarity. More recently, Kastenholz (2010) suggested that cultural distance may be an appealing factor for some and an inhibiting factor for others. However, no known studies were found to examine whether similarity-driven segments exist within a single market, such as China.

Interestingly, a parallel body of research has found evidence for a familiarity-driven segmentation (Chen & Lin, 2012). This line of research is based on a familiarity-attraction hypothesis, where positive feelings come from familiar objects, which are seen as more attractive and are judged more favorably (Skurnik et al., 2005). In contrast, people tend to approach unfamiliar objects or situations with caution, hesitation, and fear (Franzoi, 1996). Thus, following the same logic, a destination is expected to be judged more favorably when its environment is perceived to be similar or compatible to the tourist’s way of life. Tourists will find it easier to understand normative or expected behaviors when there is a closer person–environment fit, leading to less uncertainty and stress. In the case of Chinese tourists, destinations where Confucianism values are practised to some extent, may be perceived as more compatible.

The Measurement of Cultural Similarity (Distance)

In international business, cultural similarity (distance) has been examined at a country (cultural distance; CD) and an individual level (psychic distance; PD). CD captures the extent to which countries or cultures as a whole are similar or different, whereas PD captures individuals’ perceptions of these differences, which may be based on CD. CD, most commonly measured by secondary data (e.g., one or more of Hofstede’s [1980] dimensions), has been widely used to predict behavior in business research, including management, marketing, accounting, finance, and tourism, despite criticisms about the limited nature of the construct. Shenkar (2012) summarized the limitations of CD, especially when it is measured by static indexes (e.g., Kogut & Singh, 1988), noting problems with the underlying assumptions:

CD symmetry, where home and host cultures are expected to have an equal effect

CD stability over time, despite increasing levels of knowledge, familiarity, and experience

CD linearity, with expatriate literature suggesting adaptation may be U-shaped

CD causality, as it is often assumed to be the only determinant of cultural distance

CD discordance, as all dimensions of CD are commonly assumed to create an equal lack of fit

Based on these problematic assumptions, Shenkar (2012) suggested the following recommendations for CD research:

The use of Kogut and Singh’s (1988) index should be theoretically justified, tested for discordance between dimensions, and supplemented with other information

The additional use of cultural similarity clusters (e.g., Ronen & Shenkar, 1985), as these measures do not assume linearity

The supplementation of national indexes with cognitive CD measures (e.g., perceived adjustment difficulties and other measures of psychic distance)

The addition of “closing distance” measures (e.g., cultural attraction, foreign experience and acculturation, geographical distance, language, level of development, market size) as controls, as they may correlate with CD or interact with its impact

The examination of CD as a dependent, as well as an independent variable

The replacement of “distance” with “friction” as the underlying metaphor, which considers the interactions between home and host cultures

While some of these recommendations have appeared in tourism research (e.g., the use of multiple country indicators, as well as cognitive measures, as in Ng et al., 2007), others examined cultural “friction” based on the level of interaction between tourists and their host culture (Rasmi et al., 2014). Indeed, tourism behavior offers a rich environment for the examination of Shenkar’s (2012) suggestions, as it involves greater volitional behavior than is likely in international business dealings.

As previously mentioned, Bi and Lehto found an inverted U relationship between CD, based on Kogut and Singh’s (1988) index, and Chinese tourists’ destination choice. Using outbound tourism statistics from China between 1995 and 2014, they reported a CD score of 2.8727 being the optimal point for a positive CD effect, after which negative impact follows. Thus, they suggested that countries (e.g., Germany, Poland, Chile, and Canada) where CD scores were near this point could attract more Chinese outbound tourists, whereas those countries having a lower (e.g., the Philippines, Malaysia, Vietnam, Singapore, Thailand, and South Korea) or higher CD score (e.g., Sweden, the Netherlands, the United States and Japan) are less likely to attract Chinese outbound tourists. However, these findings require verification at an individual level, because culture does not correspond to national boundaries and individuals within cultures differ in their internalization of culture, as well as in their perceptions of other cultures (Hofstede, 2001). Further, Ahn and McKercher (2015) warned about the danger of assuming a single cultural profile for a country, and suggest that tourist segment identification from a single country is necessary.

The Current Study

In the current research, similarity is measured as tourists’ perceptions of cultural/lifestyle similarity with a destination, rather than relying on a country-level cultural similarity index, such as the one proposed by Kogut and Singh (1988). The use of perceptions of cultural/lifestyle similarity within a destination allows us to test for individual differences, to see whether some tourists will be driven to visit a destination by their perceptions of similarity in culture and lifestyle. Thus, Hypothesis 1 is proposed:

Hypothesis 1o: Similarity-driven segments will not be found for any destinations.

Hypothesis 1a: Similarity-driven segments will be found for all destinations, where tourists will be driven in their choice of destination by their perceived similarity of the destination’s culture and lifestyle.

In order to understand why similarity might be more or less important for destination choice, we propose a range of factors that might influence these relations, to provide a potential profile for a similarity-driven segment. Specifically, we consider the tourist’s level of ethnocentrism, travel experience, uncertainty avoidance, as well as their novelty-seeking tendency. Each of these factors may independently impact the tendency to visit more or less similar destinations.

First, we expect that tourists higher on ethnocentrism will be more likely to visit destinations they perceive as more similar, in terms of their culture and lifestyle. People high on ethnocentrism tend to view in-group members as superior and feel their in-group is the center of what is reasonable and proper in life (Brislin, 2000). Consequently, they are likely to try to maintain social distance from out-group members (Gudykunst, 1991). While studies of the effects of ethnocentrism are rare in the tourism literature, ethnocentrism has been found to be negatively related to Chinese tourists’ preference in choosing a foreign airline (Cheng, 2011) and positively related to tourists’ willingness to visit and recommend domestic tourism (Kock et al., 2019). Thus, ethnocentric tourists may limit their destination choices to those in which they perceive as having a high level of similarity in culture/lifestyle.

Second, we expect that less experienced tourists will be more likely to visit destinations they perceive as more similar, in terms of their culture and lifestyle. Research has found that inexperienced tourists tend to adopt a range of risk-reducing strategies when they visit culturally distant destinations, such as the use of tour operators and travel packages, and travelling in larger numbers (Lepp & Gibson, 2003; Litvin et al., 2004; Money & Crotts, 2003). They are also more concerned with the basics than more experienced tourists, including familiar food, health, and safety (e.g., Lepp & Gibson, 2003; Pearce, 1988), as well as the ability to speak their own language (Goeldner & Ritchie, 2006).

Third, we expect that tourists high on uncertainty avoidance (UA; Hofstede, 1980) will be more likely to visit destinations they perceive as more similar, in terms of their culture and lifestyle. People high on UA appreciate standards and norms that enable them to predict reactions and control their environment, while those low on UA are more accepting of uncertainty and tolerant of dissimilar ideas (Yoo & Donthu, 2002). In line with this, Yang and Wong (2012) found that the negative impact of cultural distance on inbound tourists to China is dependent on uncertainty avoidance, suggesting high-UA individuals may avoid high-CD destinations. Studies also reported that Chinese tourists tend to avoid visiting remote or unfamiliar destinations due to being risk-averse (Wen & Huang, 2019; Xu et al., 2008). UA has also been found to influence the use of risk-reducing travel behaviors, such as buying prepackaged tours, taking shorter trips, and visiting fewer destinations (Money & Crotts, 2003).

Finally, we expect that tourists high on novelty seeking will be less likely to visit destinations they perceive as more similar, in terms of their culture and lifestyle. Novelty seeking is often defined as “the degree of contrast between present perception and past experience, making it the opposite of familiarity” (Assaker et al., 2011, p. 891). Novelty-seeking individuals tend to look for varied, new, and intense experiences that promise new sensations (Copeland & Hamer, 1998). Tourism researchers have long suggested that novelty-seeking influences travel behavior (Cohen, 1972; McIntosh et al., 1994; McKercher & Cros, 2003; O’Leary & Deegan, 2003). Novelty-seekers tend to look for exotic destinations (Feng & Jang, 2004) and are less likely to visit the same destination (Assaker et al., 2011). However, research has suggested that novelty seeking may be related to uncertainty avoidance. Tourists from low UA were more likely to be novelty seeking in their food choices than those from high-UA cultures. Further, having at least some elements of cultural similarity may be conducive to novelty seeking, as Basala and Klenosky (2001) found that people were more likely to consider visiting a novel destination if their home language was spoken at that destination. Thus, some similarity elements may enhance the environment in which novel activities are undertaken.

In sum, those who are high in ethnocentrism, low in travel experience, high in uncertainty avoidance, or low in novelty seeking, are more likely to be classified into the similarity-driven cluster, as reflected by the following hypotheses:

Hypothesis 2o: Tourists’ level of ethnocentrism will not be related to similarity-driven clusters.

Hypothesis 2a: Tourists who are high on ethnocentrism are more likely to be members of similarity-driven clusters.

Hypothesis 3o: Tourists’ travel experience will not be related to similarity-driven clusters.

Hypothesis 3a: Tourists who are high on travel experience are less likely to be members of similarity-driven clusters.

Hypothesis 4o: Tourists’ uncertainty avoidant will not be related to similarity-driven clusters.

Hypothesis 4a: Tourists who are more uncertainty avoidant are more likely to be members of similarity-driven clusters.

Hypothesis 5o: Tourists’ novelty seeking will not be related to similarity-driven clusters.

Hypothesis 5a: Tourists who are more novelty seeking are less likely to be members of similarity-driven clusters.

Study Method

Participants and Procedures

Data were collected over the internet using a commercial Chinese consumer research panel provider, as part of a larger study of people’s travel plans. As China has a relatively low internet penetration (Internetworldstats.com) in rural areas, data were obtained from people in China’s three major cities (Beijing, Shanghai, and Guangzhou), in which internet use and infrastructure standards are significantly higher than for the country as a whole. The sample was selected to reflect a broad range of China’s adult population in terms of age, income, and gender. Respondents were screened to be permanent residents of China and potential outbound tourists, in that they had travelled internationally in the past 5 years or intended to do so in the next 2 years. In total, 205 respondents completed the online survey. Most of the respondents were aged between 25 and 34 years (46%) and 53% of the respondents were female. The median household income was in the RMB60,000 to 89,999 categories (see Table 2). While all of the respondents were permanent residents of China, almost all (99%) had been born in China.

Table 2

Respondents’ Demographic Profile

Variable	Category	Frequency	Percent
Age (years)	18-24	78	38
	25-34	94	46
	35-44	20	10
	45-54	11	5
	55-64	2	1
	65+	0	0
Gender	Male	96	47
	Female	109	53
Income	<RMB30,000	29	14
	RMB30,000-59,999	53	26
	RMB60,000-89,999	47	23
	RMB90,000-129,999	45	22
	RMB130,000-160,000	31	15
Total		205	100

After the initial screening questions, respondents were asked a series of questions about the seven travel destinations shown in Table 1, all of which were in the top 10 travel destinations for Chinese tourists (Hong Kong, Macao, and Taiwan were excluded because most of the trips involve visiting friends and/or relatives). So as not to overburden respondents, established short scales were used where possible. In each case, the six variables of interest were taken from existing scales, although these were modified to take account of the present Chinese research context. The scales chosen include the following:

Nesdale and Mak’s (2003) Single-Item Perceived Similarity Scale, which asked respondents how similar or different they felt their backgrounds were to the culture and lifestyle of the seven foreign destinations (Japan, South Korea, Vietnam, Malaysia, United States, Thailand, and Singapore)

Basala and Klenosky’s (2001) Single-Item Intention to Visit Scale, which asked respondents to rate their intention to visit each of the seven foreign destinations

Sonmez and Graefe’s (1998) Single-Item Travel Experience Scale, which asked respondents how many international holidays they had taken (other than to Hong Kong, Taiwan, and Macau) in the last 5 years. Their travel frequency was then computed and used as proxy for their travel experience

Dabholkar and Bagozzi’s (2002) Novelty-Seeking Scale; the five positively worded items were used, as previous research has suggested that negatively worded items are problematic in East Asian contexts (Wong et al., 2003)

Donthu and Yoo’s (1998) five-item Positive-Worded Uncertainty Avoidance Scale

Neuliep and McCroskey’s(1997) 12 Positive-Worded Generalized Ethnocentrism Scale

The survey also collected background information, including age, gender, income, and country of birth. After being reviewed by several experienced cross-cultural researchers, a representative from a government tourism agency and from the commercial sample provider, the survey was translated, using the back-translation method (Brislin, 1970), from English to Mandarin and back to English. Discrepancies in the two English versions were resolved with the translators to ensure consistency in the items’ meaning.

Analysis Method

Latent GOLD 4.5 was used to perform latent class regression models on visit intention to each of the seven destinations. Intention to visit was entered as a dependent variable, while perceived similarity as an independent variable. A set of covariates including ethnocentrism, travel experience, uncertainty avoidance and novelty seeking were entered to understand the profile of the similarity-driven cluster. The first step in latent class analysis involves estimating models with 1, 2, . . ., n number of latent classes/clusters and comparing the fit of each model to the data. Model fit statistics commonly used to assess best model solutions are log likelihood and associated Bayesian information criterion value (Moors, 2009). The lower the values, the better the model fit.

Study Findings

Table 3 shows the means and standard deviations for the similarity and intentions measure for each destination, as well as the percentage of respondents who had visited them. As can be seen in Table 3, the Chinese respondents felt Singapore and Korea were the most culturally similar destinations, while the Vietnam and United States were seen as the least culturally similar destinations. Respondents were also most likely to visit Singapore and Korea and least likely to visit Vietnam.

Table 3

Mean Similarity, Intention, and Geographical Distance

Country	Similarity		Intention		Percent Who Visited (Past 5 Years)	Geographic Distance^a (Miles)
Country	M	SD	M	SD	Percent Who Visited (Past 5 Years)	Geographic Distance^a (Miles)
Japan	3.88	1.59	2.86	1.19	17	740
South Korea	4.24	1.5	3.25	1.09	9	378
Vietnam	3.37	1.55	2.14	1.01	6	2,600
Malaysia	3.63	1.56	2.84	1.19	13	3,223
United States	3.02	1.6	3.03	1.16	8	6,565
Thailand	3.42	1.55	2.97	1.15	25	2,639
Singapore	4.50	1.59	3.33	1.06	20	3,272

The destination capital’s geographical distance in miles is from Beijing. Source: Geobytes city distance (http://www.geobytes.com).

Lower Bayesian information criterion value indicates a better model fit (Moors, 2009). While log likelihood values continue to go down as the number of clusters increases, given only one predictor, we chose the model with the lowest number of clusters that included a clear similarity-driven cluster. In all seven country destinations, this is achieved at two- or three-cluster solutions (see Table 4).

Table 4

Latent Class Regression Model Results for Chinese Tourists’ Intention to Visit Seven Top Outbound Destinations

	Japan		South Korea		Vietnam			Malaysia			United States			Thailand			Singapore
	Class1	Class2	Class1	Class2	Class1	Class2	Class3	Class1	Class2	Class3	Class1	Class2	Class3	Class1	Class2	Class3	Class1	Class2	Class3
R ²	.01	.53	.10	.05	.00	.40	.68	.10	.20	.13	.00	.00	.38	.03	.00	.57	.13	.03	.40
Class size	0.56	0.44	0.56	0.44	0.57	0.39	0.03	0.46	0.29	0.25	0.50	0.26	0.24	0.52	0.26	0.22	0.64	0.27	0.10
Predictors
Perceived similarity	0.03	0.94 ^a	0.23 ^a	0.16	0.02	0.56 ^a	0.62	0.41	0.20 ^a	0.29	0.02	0.01	0.76 ^a	0.11	0.02	0.96 ^a	0.25 ^a	0.09	−1.03
Intercept	0.13	−0.13	0.29	−0.29	2.44^a	1.53	−3.97^a	0.74^a	−0.15	−0.60	0.58	0.07	−0.65	1.09	−1.48	0.38	2.47 ^a	−0.13	−2.34^a
Covariates
Ethnocentrism	−.03	0.03	1.03 ^a	−1.03^a	−0.81^a	0.04	0.77	−0.17	−0.43	0.60	0.86	1.13	−2.00	−2.11^a	2.44	−0.34	4.22 ^a	−1.66	−2.57^a
Travel experience	.05	−0.05	−0.10	0.10	0.38	−0.84^a	0.46	0.00	−0.29^a	0.28^a	−2.27^a	0.93	1.34 ^a	0.42	0.14	−0.55^a	−0.30	−0.05	0.36^a
Uncertainty avoidance	−.01	0.01	0.33	−0.33	0.64	1.14 ^a	−1.78^a	−0.32	0.63 ^a	−0.31	0.06	−1.49	1.43 ^a	1.11	−1.42	0.32	1.29 ^a	−0.33	−0.96^a
Novelty seeking	.32	−0.32	0.33	−0.33	−1.65^a	−0.54	2.19	−0.09	−1.28^a	1.37^a	−1.05	2.32^a	−1.26	−4.63^a	4.12^a	0.51	1.01	−0.90^a	−0.11
Class label^b	OD	SD	SD	OD	OD	SD	OD	OD	SD	OD	OD	OD	SD	OD	OD	SD	SD	OD	OD

z value > 1.96 or <−1.96. ^bSD = Similarity-driven cluster; OD = Other-driven cluster.

As can be seen from Table 4, our model produced a clear two-cluster solution for travel to Japan and South Korea, and a clear three-cluster solution in the case of travel to the other five countries. Since we are interested in understanding whether perceived similarity is a major driver for a tourist segment in destination selection, we included only one predictor in the latent class regression model. However, doing so limits our interpretation of other-driven clusters. Clearly, there can be countless types of tourist clusters depending on the behavioral variable investigated (e.g., consumption behavior by McIntyre, 2007; attractions visited by Cohen, 1972) and the predictors (e.g., travelling group, travel arrangement, income, age, etc.) included in the study. Given the focus of the current article, only similarity-driven clusters, where perceived similarity appears as a significant predictor of travel intentions, are interpretable, as other clusters where significant predictors are unknown provide very little information.

As expected, similarity-driven clusters were present for all countries, supporting Hypothesis 1. One cluster was found in each case, where perceived similarity was a positive and significant predictor of intention to visit the country (i.e., Class 2 of Japan, Class 1 of South Korea, Class 2 of Vietnam, Class 2 of Malaysia, Class 3 of the United States, Class 3 of Thailand and Class 1 of Singapore). This similarity-driven cluster included 22% to 64% of respondents for each country, which is a substantial proportion of the tourists in our samples. Tourists in this cluster were more likely to visit the destination if their perceived similarity to the destination is high, and therefore labelled “Similarity-driven.” Since perceived similarity was not a significant predictor in any of the other clusters, there is no information in the model as to what drives these tourists, and as such they were labelled “other-driven cluster.”

Following the interpretation used by Moors (2009), covariates that are significant and produce a positive value indicate a higher probability of being classified in that cluster. Thus, the value of covariates and its significance level are used to assess whether Hypotheses 2 to 5 were supported. First, Hypothesis 2 posits that tourists who are higher on ethnocentrism are more likely to belong to the similarity-driven clusters. This was supported for travel to South Korea (1.03, p < .05) and Singapore (4.22, p < .05), providing partial support to Hypothesis 2.

Second, Hypothesis 3 posits that tourists with more travel experience are less likely to belong to the similarity-driven clusters. This was supported for travel to Vietnam (−0.84, p < .05), Malaysia (−0.29, p < .29) and Thailand (−0.55, p < .05), providing partial support for Hypothesis 3. However, the relation was reversed for travel to the United States, where individuals with more travel experience appeared to have a higher probability of being in the similarity-driven cluster (1.34, p < .05). Potential reasons (e.g., geographical distance) for this reversal are elaborated in the “Discussion” section.

Third, Hypothesis 4 posits that tourists with higher uncertainty avoidance have a higher probability of being classified in the similarity-driven clusters. This was supported for travel to Vietnam (1.14, p < .05), Malaysia (0.63, p < .05), United States (1.43, p < .05), and Singapore (1.29, p < .05), providing partial support for Hypothesis 4. Finally, Hypothesis 5 posits that tourists with higher novelty-seeking tendencies are less likely to be in the similarity-driven clusters. This is true for travel to Malaysia (−1.28, p < .05) alone, providing limited support for Hypothesis 5. Compared with other covariates, novelty seeking was the least effective in explaining the profile of the similarity-driven cluster.

Overall, the profile of tourists in the similarity-driven cluster largely matches our expectations for travel to five of the seven countries. That is, those high in ethnocentrism and uncertainty avoidance, low in travel experiences and novelty seeking are more likely to be members of the similarity-driven cluster. However, the profile of the similarity-driven cluster for travel to Japan is uncertain, as none of the covariates were significant. Further, the profile for the United States deviated from expectations in terms of travel experience, where those motivated by cultural similarity to visit the United States also have greater travel experience. Potential reasons for these deviations are elaborated in the “Discussion” section.

Discussion

Overall, the results of this study make important theoretical and practical contributions toward understanding motivations of an important segment of Chinese tourist. First, the results confirm that a sizeable segment of Chinese tourists are driven by their perceptions of cultural and lifestyle similarity in their choice of destination. Second, the results provide important profiling information that affirms that this segment is high in ethnocentrism and uncertainty avoidance, and low in travel experiences and novelty seeking. Thus, it is clear that strong homogeneous segments exist within the Chinese tourism market and these warrant further attention by researchers.

If we begin with overall analysis, it is clear from Table 3, that Chinese respondents felt Singapore and Korea were the most culturally similar destinations and their intentions to visit were highest for these countries. Furthermore, the highest proportion of the sample was also in the similarity-driven segment for these countries (64% and 56%, respectively). In contrast, Chinese respondents felt Vietnam and the United States were the least culturally similar destinations and their intentions to visit were the lowest for Vietnam. Further, the proportion of the sample in the similarity-driven segment for these countries was much lower (39% and 24%, respectively). These results provide additional support for Ng et al.’s (2007) and Jackson’s (2001) country-level finding that destinations with higher cultural similarity are more likely to be visited at macro-level analysis. However, the power of this study is in the identification of a similarity-driven segment motivating intentions to visit all seven destinations.

The similarity-driven clusters, in our sample, capture between 22% and 64% of respondents (see Figure 1). This clearly shows the strong likelihood of a significant segment of Chinese tourists, who are drawn to destinations where there is similarity in terms of language, cultural or lifestyle aspects, where lesser adaptation is needed (e.g., Basala & Klenosky, 2001; Chen, 2000). These similarity-driven tourists may also feel more familiar and at home within similar cultures (Chen & Lin, 2012). Given the interest in familiarity and destination choices (e.g., Cohen, 1972; Chen & Lin, 2012), the link between similarity and familiarity should be further explored. While they may both have similar drivers (e.g., travel experienced or a lack there of), they are clearly different constructs. That is, a destination may be familiar but seen as either culturally similar or not and similarly a destination may be culturally similar, but familiar, or not. Despite this, similarity-driven Chinese tourists may prefer to visit a destination that they are familiar and feel at home with, or if they do venture into the unknown or an unfamiliar foreign destination, they may prefer to visit a culturally similar country or to travel within the comfort of a familiar bubble, such as an organized tour from their country.

Figure 1

Similarity-Driven Cluster Size

This study also contributes to the growing segmentation literature by stressing the importance of going beyond the more tangible travel motivations, such as destination attributes (sun and beach, cultural heritage, shopping) and convenience or cost factors (geographical distance, currency exchange, flight frequency), when identifying potential segments. The expansion of viable potential segmentation bases can help destinations to create a unique position in this highly competitive market.

The fact that similarity-driven segments were present in all seven destinations, regardless of their country-level CD, suggests that the inverted U-curve relationship between CD and Chinese tourists’ destination choice found by Bi and Lehto (2018) may not hold when using personal perceptions of cultural and lifestyle similarity. Bi and Lehto (2018) suggested an optimal CD, near the midpoint, where tourists’ arrivals were highest. To each side of this optimal CD visitation declined. They argued that too little cultural distance might adversely impact tourists’ intentions and too much may be associated with greater uncertainty and fear of negative consequences, inhibiting visits. Our results clearly show that similarity drives intentions for a significant proportion of tourist from China. They also demonstrate the importance of including individual-level perceived similarity (i.e., cognitive) measures in addition to national indexes, since both may bring different results (Shenkar, 2012).

Li and Lu’s (2016) review of 80 articles showed that the Chinese culture’s influence on tourists’ behavior has not been studied sufficiently. Culture and the assessment of similarity can be defined in many ways. This study focused on the perceived “overall culture/lifestyle” similarity, which allows individuals from a source culture to differ, and provides the flexibility for respondents to use any dimensions of culture they perceive salient in destination selection and evaluation. Future research should build on this to examine more elements of perceived similarity (e.g., food, lifestyle, language use) to provide a more nuanced view of the similarity-driven segment.

This study also illustrates the danger in assuming tourism markets are homogeneous, as most prior Chinese outbound tourist studies appear to have done (Ahn & McKercher, 2015; Bi & Lehto, 2018; Li et al., 2011). We examined tourists from a single country (China), and assessed their perceptions of similarity with a variety of popular destinations. In all cases, similarity-driven segments were found, as were “other”-driven segments that need further investigation. Thus, it is clear that treating tourists from a single country as a homogeneous market risks losing important information for single country clusters.

The current study found that those with high ethnocentrism, high uncertainty avoidance, low novelty seeking or less travel experience were more likely to belong to the similarity-driven cluster. First, these results appear consistent with Gudykunst’s (1991) notion that ethnocentrism individuals are likely to maintain social distance from out-group members, as their values or beliefs may be challenged.

Second, the findings are also consistent with Yoo and Donthu’s (2002) description of high-UA people, who appreciate standards and norms, and feeling in control of their environment. Destinations perceived to be more similar, and more likely to share similar norms may enhance tourists’ level of comfort within a destination (Money & Crotts, 2003). Our results also add support for Yang and Wong’s (2012) findings that the negative impact of cultural distance is dependent on uncertainty avoidance. While China is not a high-UA culture (Hofstede, 2001), individuals within China differ on the personal importance of UA, and those high on UA are more likely to be similarity-driven tourists.

Third, less experienced tourists were also found to belong to the similarity cluster. The findings are consistent with those reported by Lepp and Gibson (2003) and Pearce (1988), where less-experienced tourists were more concerned about basic needs (e.g., food, safety, and health issues), which are likely alleviated by choosing destinations with a higher level of cultural and lifestyle similarity. However, for travel to the United States, individuals with more travel experience appeared to have a higher probability of being in the similarity-driven cluster. This could be due to the compounded effect of geographical distance. Although United States may be more preferred by those living lifestyles similar to American culture, who perceive American culture as more similar to their own, the fact that United States is geographically very far from China (6,565 miles, see Table 3), encourages only those with high travelling experiences to travel there. In short, having perceived cultural/lifestyle similarity to America, coupled with reasonable travel experience, appears to drive this segment of Chinese tourists to visit the United States.

Finally, as expected, novelty seekers did not belong to the similarity-driven cluster, which is consistent with conventional wisdom that perceived similarity has a negative influence on novelty-seekers’ decisions. Novelty seekers look for new, novel, or exotic experiences (Copeland & Hamer, 1998; Goeldner & Ritchie, 2006; Lee & Crompton, 1992); these experiences are less likely in culturally similar destinations.

In summary, this study makes important theoretical and practical contributions. It demonstrates the importance of examining potential tourist segment within a single market. Indeed, there is a sizeable Chinese tourist segment that is driven by their perceptions of cultural and lifestyle similarity in destination selection. It also reveals theoretically sound profiles of similarity-driven segments, including those high in ethnocentrism and uncertainty avoidance, and low in travel experience and novelty seeking. The findings provide important insights for destination marketers who wish to attract Chinese tourists. It also provides information that will, hopefully, encourage more tourist segmentation studies within a single source country.

Limitations and Future Research

As is always the case, the current study has several limitations. First, respondents were recruited from a commercial consumer panel provider, to facilitate identification of potential international travellers. Specifically, only those who have travelled internationally in the past 5 years or have intention to do so in the next 2 years were invited to participate in the study. However, doing so may exclude qualified nonpanel members from participating in the study. Future studies should examine the extent to which similarity-based segments occur with more representative samples. Second, the data were gathered at one point in time, which limits its ability to capture individuals’ dynamic views on other countries. It is possible that tourist’s views of cultural similarity change with geopolitical issues over time. This should be examined in future research. Third, the latent class regression analysis had lower explained variance for some destinations than others. The reasons for this should be investigated, including the addition of other potential drivers and covariates that can be simultaneously studied.

These limitations point to a few suggestions for future research. First, an in-depth evaluation of different elements of perceived similarity may help refine the understanding of how perceived similarity drives destination choice. While two countries may be perceived to have similar levels of cultural and lifestyle similarity, they may differ on other elements. For example, Singapore was perceived to be the most similar destination by the Chinese respondents in this study. This perception may be related to the language and ethnic similarity. In contrast, South Korea, which was perceived to be the next most similar destination, may be perceived as being similar along other dimensions, such as lifestyle and safety similarity. Clarification of the elements of perceived similarity, in general and for specific destinations, would help destination marketers develop strategic positioning to capitalize on the positive elements of perceived similarity that apply to their destination.

Second, researchers might consider the addition of country-specific constructs that might reveal important factors that enhance or inhibit travel for all or some tourist segments. In light of the troubled history between China and Japan, additional covariates should be included in future studies. Japanese occupation in China brought both a sense of familiarity and animosity. Campo and Alvarez (2019) noted that past country conflict may lead to feelings of animosity. The older generation who experienced the Japanese occupation in China are likely to have a stronger sense of animosity toward Japan and this feeling of animosity may restrict their desire to visit Japan (Klein et al., 1998). Thus, covariate variables, such as animosity might be relevant and revealing. Ongoing and current tensions between countries should also be examined. For instance, current tensions between the United States and China in terms of the trade deficit and the origins of the Covid-19 pandemic, may increase nationalism. This could help uncover other important aspects of destination choice, deserving of study. For instance, Chinese who have stronger nationalism/patriotism levels might have a lesser desire to visit the United States.

Third, researchers might examine Confucianism values more directly, to see whether those high on Confucianism values (i.e., harmony, respect, and Mianzi) are more likely to be driven by similarity. Although Chinese values were almost equal to Confucianism values (B. Yang, 2012), some Chinese may embrace more of a Confucianism way of life than others. Understanding how Confucianism values influence the similarity-driven cluster may help to provide a deeper understanding on Chinese tourists’ decision-making behavior, and hence, enable them to be better served.

Managerial Implications

The present study has a number of implications for practitioners. First, destination marketers might consider a strategy that stresses similarity to attract similarity-driven Chinese tourists to visit a destination. This similarity-driven segment appears to be sizeable (about 22% to 64%), suggesting it is a market segment not to be overlooked by tour operators. While some tourists may be motivated to experience differences (e.g., in weather, scenery, people, and heritage), similarity-driven tourists are more likely to be attracted by experiences that allow them to continue a similar lifestyle to what they have at home, thus, acting as a pull factor in encouraging them to visit. For example, the tour operator could design a tour for Chinese Muslims by highlighting similarity in religious practices and customer, including a wider selection of “halal” food.

Destination tourism policy makers might consider the development of “China Towns,” as the presence of a “multi-ethnic” culture may create a cultural bubble that increases feelings of comfort and security for Chinese tourists in the similarity-driven cluster. Destinations with large “China Towns” might be appealing due to higher perceived “pockets of similarity,” such as those in Vancouver and Melbourne. Marketing message might be developed to focus on elements of home culture and lifestyle similarity, such as similar language spoken, similar payment system (i.e., Alipay), similar facilities, and similar food preparation, to convey a sense of similarity, even in destinations that may have significant cultural differences.

Knowing that similarity-driven tourists are more likely to be high in ethnocentrism and uncertainty avoidance, while being low in travel experience and novelty seeking, should help destination managers in their design of specific marketing messages and travel packages that target these tourists. For instance, travel packages might include detailed itineraries to meet the uncertainty avoidance tendency of similarity-driven members. Since they also have relatively less travel experience, briefing before the trip on what to expect, what to bring and how to behave appropriately, might be appreciated and likely to increase their trip satisfaction.

Theoretical Implications

The study found that a substantial group of Chinese tourists indeed are motivated to visit a destination based on cultural/lifestyle similarity. This finding adds empirical support to the applicability of the similarity-attraction hypothesis (Byrne & Nelson, 1965), person–environment fit (Caplan, 1987) and familiarity-attraction (Skurnik et al., 2005) concepts in the tourism context. It also extends macro-level findings on the positive relationship between cultural similarity and visit intention (Jackson, 2001; Ng et al., 2007) using individual perception-level data, where we found the relationship holds true, at least in the similarity-driven cluster. This study found important profile differences between similarity-driven and other-driven clusters; those motivated by similarity were high in ethnocentrism and uncertainty avoidance and low in travel experience and novelty seeking. These findings add to the segmentation literature (e.g., G. Chen & Huang, 2018; C. Li et al., 2017) by encouraging the use of different types of psychological characteristics (i.e., ethnocentrism, uncertainty avoidance, novelty seeking) in profiling segments, rather than relying on the more common demographic (e.g., age, income), travel characteristics (e.g., travel party, travel duration), and functional benefits (e.g., Visa application, branded shopping, price) variables.

Concluding Summary

The similarity-driven clusters, present in all top seven outbound destinations included in this study, stress that cultural and lifestyle similarities are important to a substantial proportion of Chinese tourists when they make destination decisions. The study also provides individual-level analysis (i.e., perceptions of cultural and lifestyle similarity) that supports the findings from previous macro (i.e., country-level cultural distance) analysis that shows that culturally similar countries are more often visited, adding further insights to the theoretical model for travel motivation. In addition, the study demonstrated the usefulness of latent class regression analysis in understanding motivation differences and profiles. Knowing that the similarity-driven segment market is likely to be made up of those high in ethnocentrism and uncertainty avoidance, and low in travel experience and novelty seeking, provides important information for destination marketers to design more specific messages to cater to this segment of Chinese tourists.

Footnotes

ORCID iDs

Siew Imm Ng

Geoffrey N. Soutar

Sheng Wei (email: victory-wei@163.com) is an associate professor in the School of Management at Harbin University of Commerce, Harbin, China. Siew Imm Ng (email: imm_ns@upm.edu.my) is an associate professor in the School of Business and Economics at Universiti Putra Malaysia, Selangor, Malaysia. Julie Anne Lee (email: julie.lee@uwa.edu.au) is a professor at Business School, University of Western Australia, Australia. Geoffrey N. Soutar (email: geoff.soutar@uwa.edu.au) is a professors at Business School, University of Western Australia, Australia.

References

Abooali

Mohamed

(2012). Operationalizing psychological distance in tourism marketing. International Journal of Business and Management, 7(12), 173-182. https://doi.org/10.5539/ijbm.v7n12p173

Ahn

M. J.

McKercher

(2015). The effect of cultural distance on tourism: A study of international visitors to Hong Kong. Asia Pacific Journal of Tourism Research, 20(1), 94-113. https://doi.org/10.1080/10941665.2013.866586

Assaker

Vinzi

V. E.

O’Connor

(2011). Examining the effect of novelty seeking, satisfaction, and destination image on tourists’ return pattern: A two factor, non-linear latent growth model. Tourism Management, 32(4), 890-901. https://doi.org/10.1016/j.tourman.2010.08.004

Basala

Klenosky

(2001). Travel style preferences for visiting a novel destination: A conjoint investigation across the novelty-familiarity continuum. Journal of Travel Research, 40(2), 172-183. https://doi.org/10.1177/004728750104000208

Lehto

X. Y.

(2018). Impact of cultural distance on international destination choices: The case of Chinese outbound travelers. International Journal of Tourism Research, 20(1), 50-59. https://doi.org/10.1002/jtr.2152

Bochner

(2003). Culture shock due to contact with unfamiliar cultures. Online Readings in Psychology and Culture, 8(1), 1-12. https://doi.org/10.9707/2307-0919.1073

Brislin

(2000). Understanding culture’s influence on behavior (2nd ed.). Harcourt.

Brislin

R. W.

(1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185-216. https://doi.org/10.1177/135910457000100301

Byrne

Nelson

(1965). Attraction as a linear function of proportion of positive reinforcements. Journal of Personality and Social Psychology, 1(6), 659-663. https://doi.org/10.1037/h0022073

10.

Campo

Alvarez

M. D.

(2019). Animosity toward a country in the context of destinations as tourism products. Journal of Hospitality & Tourism Research, 43(7), 1002-1024. https://doi.org/10.1177/1096348019840795

11.

Caplan

R. D.

(1987). Person-environment fit theory and organizations: Commensurate dimensions, time perspectives, and mechanisms. Journal of Vocational Behavior, 31(3), 248-267. https://doi.org/10.1016/0001-8791(87)90042-X

12.

Chen

C. C.

Lin

Y. H.

(2012). Segmenting mainland Chinese tourists to Taiwan by destination familiarity: A factor-cluster approach. International Journal of Tourism Research, 14(4), 339-352. https://doi.org/10.1002/jtr.864

13.

Chen

Huang

(2018). Understanding Chinese cultural tourists: Typology and profile. Journal of Travel & Tourism Marketing, 35(2), 162-177. https://doi.org/10.1080/10548408.2017.1350253

14.

Chen

J. S.

(2000). A case study of Korean outbound travellers’ destination images by using correspondence analysis. Tourism Management, 22(4), 345-350. https://doi.org/10.1016/S0261-5177(00)00066-2

15.

Cheng

Y. H. C. C. H.

(2011). Exploring the effects of consumer ethnocentrism on preference of choosing foreign airlines: A perspective of Chinese tourists. African Journal of Business Management, 5(34), 12966-12971. https://doi.org/10.5897/AJBM11.1200

16.

CNTA China National Tourism Administration. (2011). China Tourism Statistical Bulletin 2010. Retrieved from http://www.cnta.gov.cn/html/2011-10/2011-10-25-9-0-71726.html and chinadaily.com.cn/ministries/tourism/2017-10/15/c_202798_4.htm

17.

Cohen

(1972). Towards a sociology of international tourism. Sociological Research, 39, 164-182.

18.

Copeland

Hamer

(1998). Living with our genes. Doubleday.

19.

Dabholkar

P. A.

Bagozzi

R. P.

(2002). An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. Academy of Marketing Science, 30(3), 184-201. https://doi.org/10.1177/0092070302303001

20.

Donthu

Yoo

(1998). Cultural Influences on service quality expectations. Journal of Service Research, 1(2), 178-186. https://doi.org/10.1177/109467059800100207

21.

Diehl

(1988). Social identity and minimal groups: The effects of interpersonal and intergroup attitudinal similarity on intergroup discrimination. British Journal of Social Psychology, 27(4), 289-300. https://doi.org/10.1111/j.2044-8309.1988.tb00833.x

22.

Fam

K. S.

Waller

D. S.

(2003). Advertising controversial products in the Asia Pacific: What makes them offensive? Journal of Business Ethics, 48(3), 237-250. https://doi.org/10.1023/B:BUSI.0000005785.29778.83

23.

Fam

K. S.

Yang

Hyman

(2009). Confucian/chopsticks marketing. Journal of Business Ethics, 89(3), 393-397. https://doi.org/10.1007/s10551-009-0307-6

24.

Feng

Jang

(2004). Temporal destination loyalty: A structural initiation. Advances in Hospitality and Tourism Research, 9(2), 207-221.

25.

Franzoi

(1996). Social psychology. Brown and Benchmark.

26.

Frochot

Morrison

A. M.

(2000). Benefit segmentation: A review of its applications to travel and tourism research. Journal of Travel & Tourism Marketing, 9(4), 21-45. https://doi.org/10.1300/J073v09n04_02

27.

Goeldner

C. R.

Ritchie

J. R. B.

(2006). Tourism: Principles, practices, philosophies (10th ed.). Wiley.

28.

Gudykunst

W. B.

(1991). Bridging differences: Effective intergroup communication. Sage.

29.

Hoare

R. J.

Butcher

O’Brien

(2011). Understanding Chinese diners in an overseas context: A cultural perspective. Journal of Hospitality & Tourism Research, 35(3), 358-380. https://doi.org/10.1177/1096348010384599

30.

Hofstede

(1980). Culture’s consequences: International differences in work-related values. Sage.

31.

Hofstede

(2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage.

32.

Jackson

(2001). Cultural influences on tourist destination choices of 21 Pacific Rim nations [Paper presentation]. CAUTHE National Research Conference, Australia.

33.

Kastenholz

(2010). “Cultural proximity” as a determinant of destination image. Journal of Vacation Marketing, 16(4), 313-322. https://doi.org/10.1177/1356766710380883

34.

Kau

A. K.

Lim

P. S.

(2005). Clustering of Chinese tourists to Singapore: An analysis of their motivations, values and satisfaction. International Journal of tourism research, 7(4-5), 231-248. https://doi.org/10.1002/jtr.537

35.

Keating

B. W.

Kriz

(2008). Outbound tourism from China: Literature review and research agenda. Journal of Hospitality and Tourism Management, 15(1), 32-41. https://doi.org/10.1375/jhtm.15.32

36.

Klein

J. G.

Ettenson

Morris

M. D.

(1998). The animosity model of foreign product purchase: An empirical test in the People’s Republic of China. Journal of Marketing, 62(1), 89-100. https://doi.org/10.1177/002224299806200108

37.

Kock

Josiassen

Assaf

A. G.

Karpen

Farrelly

(2019). Tourism ethnocentrism and its effects on tourist and resident behavior. Journal of Travel Research, 58(3), 427-439. https://doi.org/10.1177/0047287518755504

38.

Kogut

Singh

(1988). The effect of national culture on the choice of entry mode. Journal of International Business Studies, 19(3), 411-432. https://doi.org/10.1057/palgrave.jibs.8490394

39.

Kwek

Lee

Y. S.

(2010). Chinese tourists and Confucianism. Asia Pacific Journal of Tourism Research, 15(2), 129-141. https://doi.org/10.1080/10941661003629946

40.

Lee

T. H.

Crompton

(1992). Measuring novelty seeking in tourism. Annals of Tourism Research, 19(4), 732-751. https://doi.org/10.1016/0160-7383(92)90064-V

41.

Lepp

Gibson

(2003). Tourist roles, perceived risk and international tourism. Annals of Tourism Research, 30(3), 606-624. https://doi.org/10.1016/S0160-7383(03)00024-0

42.

McCabe

Chen

(2017). Destination choice of Chinese long-haul outbound tourists and market segmentation. Journal of China Tourism Research, 13(3), 298-315. https://doi.org/10.1080/19388160.2017.1398118

43.

Y. T.

(2016). Chinese culture in tourist research: A review and comparison of Chinese and English studies in 1993-2012. Tourism Review, 71(2), 118-134. https://doi.org/10.1108/TR-05-2015-0021

44.

Zhang

Mao

Deng

(2011). Segmenting Chinese outbound tourists by perceived constraints. Journal of Travel & Tourism Marketing, 28(6), 629-643. https://doi.org/10.1080/10548408.2011.598747

45.

Litvin

S. W.

Crotts

J. C.

Hefner

F. L.

(2004). Cross-cultural tourist behaviour: A replication and extension involving Hofstede’s uncertainty avoidance dimension. International Journal of Tourism Research, 6(1), 29-37. https://doi.org/10.1002/jtr.468

46.

McIntosh

Goeldner

Ritchie

(1994). Tourism: Principles, practices, philosophies (7th ed.). Wiley.

47.

McIntyre

(2007). Survival theory: Tourist consumption as a beneficial experiential process in a limited risk setting. International Journal of Tourism Research, 9(2), 115-130. https://doi.org/10.1002/jtr.598

48.

McKercher

Cros

H. D.

(2003). Testing a cultural tourism typology. International Journal of Tourism Research, 5(1), 45-58. https://doi.org/10.1002/jtr.417

49.

Moghaddam

F. M.

Stringer

(1987). Out-group similarity and intergroup bias. Journal of Social Psychology, 128(1), 105-115. https://doi.org/10.1080/00224545.1988.9711689

50.

Money

R. B.

Crotts

J. C.

(2003). The effect of uncertainty avoidance on information search, planning, and purchases of international travel vacations. Tourism Management, 24(2), 191-202. https://doi.org/10.1016/S0261-5177(02)00057-2

51.

Moors

(2009). Ranking the ratings: A latent-class regression model to control for overall agreement in opinion research. International Journal of Public Opinion Research, 22(1), 93-119. https://doi.org/10.1093/ijpor/edp036

52.

Nesdale

Mak

A. S.

(2003). Ethnic identification, self-esteem and immigrant psychological health. International Journal of Intercultural Relations, 27(1), 23-40. https://doi.org/10.1016/S0147-1767(02)00062-7

53.

Neuliep

J. W.

McCroskey

J. C.

(1997). The development of a U.S. and Generalized Ethnocentrism Scale. Communication Research Reports, 14(4), 385-398. https://doi.org/10.1080/08824099709388682

54.

S. I.

Lee

J. A.

Soutar

G. N.

(2007). Tourists’ intention to visit a country: The impact of cultural distance. Tourism Management, 28(6), 1497-1506. https://doi.org/10.1016/j.tourman.2006.11.005

55.

Nielsen. (2017). 2017 Outbound Chinese Tourism and Consumption Trends. Retrieved from https://www.nielsen.com/my/en/

56.

O’Leary

Deegan

(2003). People, pace, place: Qualitative and quantitative images of Ireland as a tourism destination in France. Journal of Vacation Marketing, 9(3), 213-226. https://doi.org/10.1177/135676670300900302

57.

Pearce

P. L.

(1988). The Ulysses factor: Evaluating visitors in tourist settings. Springer-Verlag. https://doi.org/10.1007/978-1-4612-3924-6

58.

Pearce

P. L.

M. Y.

Osmond

(2013). Puzzles in understanding Chinese tourist behaviour: Towards a triple-C gaze. Tourism Recreation Research, 38(2), 145-157. https://doi.org/10.1080/02508281.2013.11081741

59.

Plog

S. C.

(1974). Why destination areas rise and fall in popularity. Cornell Hotel and Restaurant Administration Quarterly, 14(4), 55-58. https://doi.org/10.1177/001088047401400409

60.

Rasmi

Lee

J. A.

Soutar

G. N.

(2014). Tourists’ strategies: An acculturation approach. Tourism Management, 40, 311-320. https://doi.org/10.1016/j.tourman.2013.07.006

61.

Rokeach

Smith

P. W.

Evans

R. I.

(1960). Two kinds of prejudice or one. In Rokeach

(Ed.), The open and closed mind (pp. 132-168). Basic Books.

62.

Ronen

Shenkar

(1985). Clustering countries on attitudinal dimensions: A review and synthesis. Academy of Management Review, 10(3), 435-454. https://doi.org/10.5465/amr.1985.4278955

63.

Shenkar

(2012). Cultural distance revisited: Towards a more rigorous conceptualization and measurement of cultural differences, Journal of International Business Studies, 43(1), 1-11. https://doi.org/10.1057/jibs.2011.40

64.

Skurnik

Yoon

Park

D. C.

Schwartz

(2005). How warnings about false claims become recommendations. Journal of Consumer Research, 31(4), 713-724. https://doi.org/10.1086/426605

65.

Sonmez

S. F.

Graefe

A. R.

(1998). Determining future travel behaviour from past travel experience and perception of risk and safety. Journal of Travel Research, 37(2), 171-177. https://doi.org/10.1177/004728759803700209

66.

Min

Chen

M. H.

Swanger

(2018). Cultural characteristics and tourist shopping spending. Journal of Hospitality & Tourism Research, 42(8), 1210-1231. https://doi.org/10.1177/1096348017731131

67.

Wen

Huang

(2019). Chinese tourists visiting volatile destinations: Integrating cultural values into motivation-based segmentation. Journal of China Tourism Research, 15(4), 520-540. https://doi.org/10.1080/19388160.2019.1589610

68.

Wong

Rindfleisch

Burroughs

J. E.

(2003). Do reverse-worded items confound measures in cross-cultural consumer research? The case of the material values scale. Journal of Consumer Research, 30(1), 72-91. https://doi.org/10.1086/374697

69.

World Tourism Organization. (2019). International tourism highlights 2019 edition. https://www.e-unwto.org/doi/pdf/10.18111/9789284421152

70.

M. Y.

Pearce

P. L.

(2014). Chinese recreational vehicle users in Australia: A netnographic study of tourist motivation. Tourism Management, 43, 22-35. https://doi.org/10.1016/j.tourman.2014.01.010

71.

(2019, August 7). China Tourism Academy (CTA): Annual Report on China Outbound Tourism Development 2019. https://dragontrail.com/resources/blog/cta-annual-report-on-china-outbound-tourism-development-2019

72.

Ding

Packer

(2008). Tourism research in China: Understanding the unique cultural contexts and complexities. Current Issues in Tourism, 11(6), 473-491. https://doi.org/10.1080/13683500802475737

73.

Yang

(2012). Confucianism, socialism, and capitalism: A comparison of cultural ideologies and implied managerial philosophies and practices in the PR China. Human Resource Management Review, 22(3), 165-178. https://doi.org/10.1016/j.hrmr.2012.01.002

74.

Yang

Liu

(2016). The influences of national cultural distance on international destination choice of Japanese and Chinese residents. Tourism Tribune, 31(10), 45-55.

75.

Yang

Wong

K. K. F.

(2012). The influence of cultural distance on China inbound tourism flows: A panel data gravity model approach. Asian Geographer, 29(1), 21-37. https://doi.org/10.1080/10225706.2012.662314

76.

Yoo

Donthu

(2002). The effects of marketing education and individual cultural values on marketing ethics of students. Journal of Marketing Education, 24(2), 92-103. https://doi.org/10.1177/0273475302242002