Market Segmentation by Travel Motivations under a Transforming Economy: Evidence from the Monte Carlo of the Orient

Abstract

Macau, the world’s largest casino hub with the largest gambling revenues, has received increasing attention as a research focus. Macau attracts more and more Chinese outbound tourists each year due to its gambling industry monopoly in Greater China. Macau is positioning itself as a ‘world center of tourism and leisure’ and has set out plans to become a broader-based tourist destination with economic diversification. Thus, an understanding of people’s varied motivations plays an important role in the current status of an environment with a moderate diversification of economic development. The objective of this study is to classify the outbound mainland Chinese tourists in Macau into more homogeneous subgroups on the basis of their travel motivations. Thirteen motivation items are extracted into four factors (namely knowledge and culture, relaxation, entertainment and gambling, and prestige) through exploratory factor analysis. Three distinct market segments are identified—freedom seekers, multi-purpose seekers, and fun and special interest in gambling seekers—based on a cluster analysis using k-means methodology. This study also presents the socio-demographic and trip characteristic differences among these three segments.

1. Introduction

Market segmentation is an important strategy for developing products and marketing materials targeted towards different groups with varying needs and interests. It helps the industry to understand the subgroups that make up the audience so that marketers can better tailor products and services. Segmentation studies are proliferating in the industry as well as in tourism research. In the last three decades, tourism market segmentation has received considerable attention. It is widely known by scholars and practitioners that a good grasp of the present market’s segmentation is necessary for efficient travel marketing and management [1,2]. Travel brands and destination marketers use segmentation information to identify their most profitable segments and better understand what they really want. Market segmentation and promotion is considered to be an effective strategic marketing tool for marketers to customize their marketing programs. Tourist market segmentation has become a widespread marketing strategy for destination marketers who aim to form a competitive edge by recognizing the appropriate segments of tourists and providing them with the travel services that best meet their needs and wants [3].

Socio-demographic and trip characteristics (e.g., age, gender, travel duration, travel purpose, trip pattern, etc.) are generally adopted as the criteria for segmentation. Nevertheless, today’s tourists are becoming ever more difficult to describe simply in terms of their socio-demographic and trip characteristics [4,5,6], especially under the circumstance that most of the destinations offer a range of tourism products, facilities, and services to attract tourists. On the other hand, from the view point of the competitiveness of a destination, it is critical to explore the evaluative factors influencing the destination choices of the tourists and understand how they behave when selecting a destination [7,8,9,10,11]. Due to the characteristics of feasibility, generalization, and reasonability, tourist motivation—as one of the specific segmentation variables—is extensively applied in segmenting markets for destinations. Tourist motivation plays a vital part in the development of destination marketing and further influences the tourists’ purchasing behavior [12,13]. A deep understanding of tourist motivations would enable destination marketers to augment their offers in ways that satisfy tourists. Several recent papers have described the heterogeneity in tourist motivations and highlighted the importance of exploring the motivations behind tourists’ decisions [14]. The context-dependency of tourist motivations for a specified destination suggests areas in which further research needs to be conducted. For example, prior research has demonstrated that analyzing destination attributes which differ from one destination to another can be a good way of developing an understanding of tourist motivations [15]. Several studies have focused on tourist motivations in the context of gambling destinations [1]. Topics such as tourist behavior, tourist motivation, and pursued benefits have been mostly highlighted in relation to gambling destinations.

Macau and Hong Kong, which lie on the southern coast of China, are both Special Administrative Regions (SARs) of China. The travel industry has contributed a lot to the economic development of the society by creating job opportunities [16,17]. Macau, in particular, has been witnessing a constant rise in inbound tourist numbers after its return to Chinese sovereignty in 1999. Macau is an offshore financial center and an independent tariff zone where Eastern and Western cultures have coexisted peacefully for more than 400 years. The gambling industry has been a monopoly for over a century, since the legalization of gaming in 1847 by the Portuguese administration, issuing gaming licenses, which has become the largest pillar of Macau’s economy [18]. A mix of Cantonese culture and Portuguese heritage has long attracted visitors from all across the world. After a rapid expansion in gambling, Macau has become one of the most popular gambling markets in the world [19]. However, the gambling industry in Macau has been facing various challenges [20]. Macau is now faced with a set of unique challenges regarding its geographic location, offshore online gambling sites, size constraints, regional competitions, etc. For example, there is an ongoing and intense competition among recreational destinations in Southeast Asia to attract more mainland Chinese tourists. The steady increase of gambling revenues in Macau is greatly threatened by the appearance of new gaming destinations in Singapore. Thus, Macau could lose its competitive edge, and then it will be more difficult for it to sustain its market competitiveness.

Mainland Chinese tourists are an important and reliable source of revenue in Macau [21]. Prior research shows that the gambling opportunity is considered one of the major tourist motivations for mainland Chinese tourists in Macau [22]. However, with the anti-corruption storm in mainland China since the end of 2012, the revenue of the gambling industry has decreased considerably in Macau, which affects the economic sustainability in the area. Due to the increasing pressure of the economic slowdown, the Macau government is attempting to position this city as a broader-based tourist destination. The SAR government’s strategy of economic diversification is transforming the economic environment of this gambling destination. Against this background, it is necessary to continue studying and analyzing the characteristics and preferences of mainland Chinese tourists in Macau from a broader perspective beyond gambling, and to design services and products that cater for their various wants and demands. Based on the current tourism situation of Macau, this study aims to segment and generalize Chinese outbound tourists in terms of their motivations to Macau and assist destination marketers in formulating suitable marketing strategies. The paper is structured as follows. Section 1 describes the aims for this study. Section 2 gives an overview of the related studies. Section 3 presents the research design of this study. Section 4 discusses the core findings of this study based upon the methodology. Finally, Section 5 presents the conclusions and limitations of this study.

2. Literature Review

2.1. Tourist Motivation

Motivation can be understood as an ultimate force that causes individuals to take action [5]. Motivation is based on biological needs and performance-related goals which could affect their attitudes and behaviors [23,24]. Tourist motivation is a basic psychological process which plays a fundamental part in the mechanics of tourism analysis. Motivation has emerged as one of the most popular segmentation bases in prior tourism studies [25,26,27]. Tourist motivation refers to a set of biological and cultural forces that cause an individual to participate in tourism activities. There are five intrinsic elements within tourist motivation: escape, security, relationship, self-esteem, and self-actualization [28,29]. A large body of tourism literature emphasizes that push and pull factors have a significant impact on a person’s motivation. For example, push factors refer to inner impellent demands to escape from the routine surroundings, and the pull factors can be defined as the outer elements that appeal to visitors in relation to particular destinations [30,31]. In particular, push factors are specified as origin-associated, invisible, and inherent wishes of the individual tourists, such as wishes for freedom, rest, leisure, exploration, health, and reputation, whereas the pull factors generally refer to the charm of a specific destination and visible features such as coastal beaches, hotels, and entertainment facilities, as well as cultural and historical resources. As for the benefits gained by traveling, they can be both personal and/or interpersonal. The personal benefits refer to the self-determination realized by the visitors themselves, while the interpersonal benefits refer to the social interactions with different individuals. For example, tourists can not only meet strangers, visit new places, and acquire different cultural experiences, but they also escape from their boring daily life.

Tourist motivation plays a significant role in influencing travel decisions and tourism marketing [32,33,34,35]. Tourist motivation can be analyzed through a particular group of target tourists who are senior or backpackers. According to the studies by Goeldner and Ritchie [36], tourist motivation can be categorized into four affective levels: (a) corporeal, such as relaxation; (b) mental, such as exploring distinctive geographical regions; (c) interpersonal, such as encountering and interacting with strangers; and (d) reputation, such as self-respect and self-realization. By studying the tourist motivation of Chinese tourists to Australia, Wu and Pearce [35] pointed out six driving push elements: novelty-seeking, relation, escape, self-development, ego enhancement, and particular interests. Furthermore, they determined the three marketing segments of sports pursuers, novelty pursuers, and family and leisure pursuers. Park and Yoon [6] concluded that there are four different driving market segments: family togetherness, passive tourists, want-it-all tourists, and study and excitement. Nevertheless, as these studies focus only on the regular destinations, there is a lack of discussion on the tourist motivations under a transforming market in a tourist-generating region and how to transform the economy strategy and policy in a destination with gambling as its main destination appeal.

2.2. Tourist Segmentation

Market segmentation has been one of the primary contributors to the development of effective marketing programs [37]. It provides a unique identification method to separate a specific group of customers that have similar purchasing behaviors and attitudes [38]. The primary objective of segmentation is to partition the total market into relatively homogeneous clusters with similar consumption patterns [39]. A certain amount of marketing information collected about a particular market segment plays a crucial part in the overall corporate strategy [40]. Through the process of market segmentation, firms can divide large heterogeneous tourist markets into small ones that can be reached more efficiently [41]. For example, market segmentation could be implemented to better understand the competitive landscape since it can provide insightful information on visitor preferences and behavior and strive to match tourism demand by enhancing product offerings.

Tourist segmentation refers to the process of assembling a group of available tourists with the same consumption demands, interests, and hobbies through marketing programs [42,43]. There are quite a number of potential variables used to segment a consumer market. The most common bases used to group markets are socio-demographic variables (e.g., educational level, gender, age, family size, monthly income, etc.), geographical variables (e.g., local and non-local residents), behavioral variables (e.g., preferences, usage frequency, brand loyalty, etc.), and psychographic variables (e.g., interests, activities, opinions, personality, etc.). A large body of tourism literature has used demographic characteristics, activities, travel expenditure, benefits, and motivation to segment tourists. Trip purpose has also been used in an attempt to segment different types of tourists. For instance, Zhang and Marcussen [2] segmented the tourist market into four categories: individual business trips, government or enterprise business trips, visiting relatives and friends, and leisure trips. Comparable results have been reported when psychographics aid in market segmentation. The variable of motivation is a highly-welcomed and efficient approach to subdivide tourists. For example, based on a Portuguese social travel show for seniors, Carneiro et al. [32] distinguished a motive-oriented segmentation. Through the cluster analysis of the driving elements, three clusters are identified: the passive seniors, the social and cultural seniors, and the positive seniors. Dey and Sarma [43] revealed the function of message sources among diverse motive-oriented segmentations of tourists in the increasingly popular travel attractions of northeastern India. Using the factor-cluster segmentation method, three motivation-oriented travelers with different natures are identified. In addition, the duration of trip has also been used for tourism segmentation: for example, Neal [44] classified the research targets as short term (e.g., six-night stay) and long-term visitors (e.g., more than a one-week stay).

There are several studies on segmentation for a gaming destination. Lee et al. [1] adopted a cluster analysis, endeavoring to divide the casino gambling markets and discover variations among segments in regard to social demographics and behavioral factors. By applying the factor analysis, the four motivation aspects describing the casino gambling are socialization, challenge, relaxation, and winning. Four groups (e.g., challenge pursuers, only-winning gamblers, light gambling customers, and multi-purpose pursuers) are then identified based on a traditional cluster analysis. Vong [14] found that there are quite a number of cultural tourists in a typical gambling destination such as Macau. Based on the results of previous studies from various fields, this study attempts to answer the following questions:

(1)

What are the motivations of mainland Chinese tourists to Macau in the new era of consumption transformation after the anti-corruption storm, as well as under the SAR government’s strategy of economic diversification?

(2)

What types of tourist groups are segmented, according to the multiple motivation factors above?

(3)

What are the most effective marketing strategies which could serve as a crucial road map to help Macau maintain its economic stability in the long run?

3. Methodology

3.1. Data Collection

A survey was conducted recently in Macau. The target respondents in this study are mainland Chinese travelers of more than 18 years old who have traveled to Macau. The four most-populated areas in Macau were selected: departure regions, historic and cultural spots, shopping malls, and meeting incentive conference exhibition (MICE) centers. A total of 600 questionnaires were distributed using convenience sampling, of which 496 usable samples were returned. The total response rate was 82.7%.

3.2. Measurement

Items used in the operationalization of tourist motivation were drawn from relevant prior research [28,35,43,44,45,46]. To guarantee the explicitness, effectiveness, and comprehensiveness of the original questionnaire, a total of 25 postgraduates were invited to join a pilot study prior to the formal data collection. The questions in existing surveys were modified and finalized according to the feedback from this pilot study. Therefore, the content validity of the questionnaire was considered as reliable. Participants were required to grade the significance of 14 statements regarding their traveling motivations. Responses to these 14 items used a 7-point Likert scale with ‘neutral’ as the middle option.

3.3. Analysis

The data analysis can be divided into five steps. First, descriptive analysis was used to describe the basic features of the data in this study. Second, principal component analysis (PCA) with varimax rotation method was used to reduce tourist motivation dimensionality and explore its possible underlying factors. The extracted factors which had a high eigenvalue remained when performed using item correlation matrices [47]. In addition, retained factors should have at least three indicators with their loadings greater than 0.5. Furthermore, the Cronbach’s alpha coefficient was calculated to evaluate the internal reliability of each factor. A reliability of factors of more than 0.7 was considered acceptable. Thirdly, a clustering analysis was implemented to divide the respondents based on their motivations. Multivariable statistics were employed to inspect any obvious variations among the clusters. For example, Scheffe multiple-range tests were carried out to check for any variations among these diversified clusters. Fourthly, discriminant analysis was used to determine independent variables related to the dependent variable and to assess the adequacy of a classification from a set of metric predictors. Finally, chi-squared tests were implemented to examine any obvious variations among the clusters regarding the tourists’ social-demographic profiles and trip characteristics.

4. Results

4.1. Sample Profile

Most respondents were under 40 years of age, with females (50.8%) having a slight majority over males. More than half of the respondents (59.3%) held a bachelor degree or above. Among the respondents surveyed, 26.3% were employees and 26.3% were students. With regard to monthly family income, 68.5% of the investigation samples earned less than RMB 20,000. In addition, 28.0% traveled with family members and relatives, 43.8% traveled with friends, colleagues, and classmates, and 42.8% were revisiting Macau.

4.2. Principal Components Analysis

As mentioned above, only the items with factor loadings of 0.4 or above, or the ones with eigenvalues larger than 1, were retained. Due to low communalities (0.30), the item “to explore its natural environment” among the 14 items in the initial scale was removed from the scale. Therefore, only 13 items from the initial scale were incorporated into the ultimate factor solution. Four eigenvalues account for 86.15% of the total variance based on the results of the principal components factor analysis (See

Table 1. Exploratory factor analysis of motivations.

Image Item	Factor Loading				Communality	Cronbach’s α
Factor 1—Knowledge						0.91
Experience new cultures/ways of life	0.84				0.84
Learn new things, increase my knowledge	0.87				0.91
Discover different new places	0.88				0.92
Explore historical and cultural heritage	0.88				0.91
Factor 2—Relaxation						0.89
Rest and relaxation		0.90			0.91
Alleviate stress and tension		0.86			0.88
Escape everyday routine		0.89			0.90
Factor 3—Entertainment						0.83
Seek recreation and entertainment			0.81		0.91
Able to gamble legally			0.86		0.92
Visit places with good casinos			0.86		0.92
Factor 4—Prestige						0.96
Go to fashionable places				0.92	0.93
Go to places that friends have not visited				0.91	0.91
Tell the experiences to friends/relatives				0.89	0.92
Variance explained (%): 86.15
Kaiser–Meyer–Olkin measure of sampling adequacy: 0.88
Bartlett’s test of sphericity: 10093.36
Significance: 0.00

). The internal consistency of each factor was evaluated by Cronbach α coefficient. The resulting α coefficient of reliability ranged from 0.83 to 0.91, which reflected an acceptable level of each measure’s reliability. Factor scores were computed by taking the average of items within each factor (see Table 1).

The first factor, which accounted for 47.05% of the total variance, was labeled as ‘knowledge’ (four items, α = 0.91). The second factor relates to ‘relaxation’ (three items) and accounted for 24.17% of the variance (α = 0.89). The third factor consists of characteristics of the ‘entertainment’ (four items) and accounted for 9.26% of the variance (α = 0.83). The fourth factor relates to ‘prestige’, which accounted for 5.67% of the variance (three items, α = 0.96).

4.3. Segmenting Tourists in Gaming Destinations

Cluster analysis was used to group underlying participants into a set of distinct segments using the Ward criterion function with a k-means method. In order to distinguish the categorization of targeted groups, this study adopted the four factors identified in exploratory factor analyses as composite variables. Three meaningful subgroups were detected based on the final result of the cluster analysis. A significant one-way ANOVA result further highlighted the extracted factors distinguishing these three identified subgroups (p < 0.001). Moreover, this study adopted Scheffe post hoc tests to examine variations among these three groups regarding each extracted factor. In addition, there were statistically significant differences among these three subgroups on the basis of the Scheffe tests.

The first group placed the highest average score on ‘relaxation’ compared to the other three groups. The first group was then named ‘freedom seekers’. This cluster was mostly attracted by the opportunity of getting away from a long-term heavy work load. The second group gave their lowest mean scores to entertainment and gambling and their highest mean score to other factors. Compared to the other groups, they were more likely be motivated by boredom alleviation, sensation seeking, socialization, etc. Thus, this group was named ‘multi-purpose seekers’. In addition, the last group placed the highest average score on entertainment compared to other groups. That is, they tended to be motivated by gambling opportunities. Therefore, this group was labeled ‘fun and special interest in gambling seekers’ (see

Table 2. Summary statistics of cluster analysis of tourist motivations.

	Cluster I (n = 217)	Cluster II (n = 141)	Cluster III (n = 138)	F-Value	Scheffe Multiple Range Tests
					I–II	I–III	II–III
Knowledge	4.37 a	6.51	4.69	103.11 ***	0.00	0.00	0.00
Relaxation	6.28	5.38	4.57	112.20 ***	0.00	0.00	0.00
Entertainment	4.69	2.01	6.49	1362.73 ***	0.00	0.00	0.00
Prestige	4.59	5.87	4.51	896.38 ***	0.00	0.17 b	0.00
Cluster name	freedom seekers	multi-purpose seekers	fun & special interest in gambling seekers

*** p < 0.001; ^a: Mean values measured on the basis of seven-point Likert scale (1: strongly disagree, 7: strongly agree); ^b: not significant.

).

Discriminant analysis was performed to validate the results obtained from the cluster analysis. Two statistically significant discriminant dimensions were specified. Two canonical discriminant functions were then computed based on the discriminant analysis with four tourist motivation factors. The canonical correlations were 0.92 and 0.83, demonstrating a positive relationship between these multivariate sets of variables (see

Table 3. Result of discriminant analysis of tourist motivation clusters ^a.

	Discriminant Function	Eigenvalue	Canonical Correlation	Wilks’ Lamda	χ2 Significance
Motivation	1	9.37	0.92	0.03	0.00
Factors	2	1.84	0.83	0.39	0.00
Standardized canonical discriminant function coefficients			Function 1	Function 2
Knowledge			0.06	−0.29
Relaxation			0.23	0.62
Entertainment			0.89	0.37
Prestige			−0.53	0.77

^a: 97.6% of original grouped cases correctly classified. 97.3% of cross-validated grouped cases correctly classified.

). The root (eigenvalue) for each discriminant function further supported the canonical correlation. Note that a higher eigenvalue indicates a considerable shared variance. Hence, the first discriminant function corresponds to the first eigenvalue (9.37) and accounted for the largest proportion of variance. Wilk’s Lambda test was also performed to explore which variable contributed significance in discriminant functions. The closer to zero the statistic is, the more the variable in question contributes to the model. The results of the chi-squared test also demonstrated that each function predicted the classification accurately (p < 0.05). Furthermore, it turned out that approximately all (97.6%) of the 496 respondents were accurately classified by the discriminant function.

4.4. Cluster Differences by Travelers’ Characteristics

A chi-squared test was carried out on the data to determine whether there are significant differences in terms of the three clusters’ socio-demographic and trip characteristics. No statistically significant differences were found among these three subgroups regarding their travel arrangement and gender. However, it appears there are significant differences regarding their age, duration of trip, etc. It was found that most of the freedom seekers and fun and special interest in gambling seekers were ages from 20 to 39, whereas most multi-purpose seekers were 50 years old or older. Thus, it was self-evident that the multi-purpose seekers were older than the fun and special interest in gambling seekers, while the fun and special interest in gambling seekers were usually older than the freedom seekers. As for the length of stay of their trips to Macau, of the total of 496 respondents, 323 were one-day tourists and 173 spent more than one day there. In terms of multi-purpose seekers, only 23.6% of visits were same-day stayers, while 76.6% were overnight stayers. This distinctly reflected the fact that most of the fun and special interest in gambling seekers would spend several days in Macau. In addition, the fun and special interest in gambling seekers generally tended to be independent without companions, while the freedom seekers and multi-purpose seekers tended to travel more frequently with their friends and relatives.

5. Discussion and Conclusions

5.1. Theoretical Implications

The overall study objective is to identify the up-to-date motives of Chinese outbound tourists to Macau under the new economic circumstance of the anti-corruption storm in mainland China and an attempt for economic diversification in Macau. The Macau SAR government has set out plans to pursue a policy of moderate economic diversification to reduce its dependence on a single gambling industry. The results of this study suggest that the current ‘one-size-fits-all’ strategy in Macau’s gambling market may not be appropriate. It is essential to bring a new understanding of the noticeable variations in response to the differences across tourist segments in terms of their travel motivations. Three major theoretical contributions result from this study’s findings.

First, through the principal components analysis (PCA), four types of motivations among mainland Chinese tourists were recognized, namely ‘knowledge’, ‘relaxation’, ‘entertainment’, and ‘prestige’. Two of the dimensions are typical push factors from tourists generating areas, including ‘relaxation’ and ‘prestige’. ‘Relaxation’ represents the situation of tourists who are attempting to escape from their everyday routine, alleviate stress and tension, and get temporary rest and relaxation, while ‘prestige’ illustrates the motive of some of the tourists from mainland China visiting Macau to take the touring experience as a symbol to improve their reputation and image in the origin area. In contrast, ‘knowledge’ and ‘entertainment’ are the pull factors from the gambling destination of Macau. Specifically, it should be noted that ‘culture and knowledge’ is an assignable factor in this cross-cultural gambling destination, which reconfirms the idea that cultural tourists are an important component of Macau’s tourism industry [14].

Second, on the basis of the identified motivation factors, this study further implemented a cluster analysis with a k-means procedure. K-means cluster analysis revealed three distinct groups, which were labeled as ‘freedom seekers’ (n = 217, 43.8%), the ‘multi-purpose seekers’ (n = 141, 28.4%), and the ‘fun and special interest in gambling seekers’ (n = 138, 27.8%). The first group of ‘freedom seekers’ was driven by getting away from their tiring routine life and pursuing freedom with this trip. In particular, this group of tourists was basically attracted by the wish to escape from their daily life and work, to relax, and enjoy getting together with their relatives, family members, friends or classmates. The second segmentation of ‘multi-purpose seekers’ was motivated by self-improvement, socialization, and relationships, as well as escape and freedom via this travel. They tended to pursue different and uncommon experiences that were inconsistent with their previous life experiences. Specifically, they value the experience of unfamiliar and unique destinations and the chance of acquainting themselves with new friends, regions, culture, and traditions. The third group of ‘fun and special interest in gambling seekers’ was usually attracted by the chance of gambling. Most of them can safely enjoy the slot machines and table games in casinos. Thus, gambling could be a healthy activity or an enjoyable form of recreational activity.

Thirdly, significant variations in social-demographic and trip characteristics were also identified among these three clusters. For example, freedom seekers are most-often day-trippers, with an age under 39, who are under the stress of everyday work with less leisure time. Only 15% of the ‘fun and special interest in gambling seekers’ are day-trippers, and 82% are aged 20 to 39. This illustrates that seeking gambling opportunities is the major motivation for this cluster. The majority of multi-purpose seekers are elderly people, 76% of them with an age over 50, which means they might have more leisure time than the other two groups. The current segmentation by travel motivations of mainland Chinese tourists to Macau seems more consistent with the new strategy and vision of the economic diversification in the ‘Monte Carlo of the Orient’. The consistency is represented in two ways. First, although gambling seekers are still an important part of the total tourists, groups of tourists have appeared who are seeking for other attractions, such as the relaxed atmosphere and environment, entertainment, and leisure facilities in Macau. Beside the way of life and the historical and cultural sites, gambling also attracts quite a number of mainland Chinese tourists to Macau. This diversity of tourists increases the economic stability of Macau, compared with a gambling- dominant economy. Second, the groups of ‘fun and special interest in gambling seekers’ treated gambling in a more touristic way, by considering gambling not only for winning, but also for experiencing (for fun). These findings will help Macau to work out more practical and effective marketing strategies under the current transforming environment.

5.2. Managerial Implications

The results and summaries drawn in this study can assist the policy makers in developing strategies to deal with the incoming challenges, so as to maintain a steady development of the overall economy and sustainable growth in the long run as a gambling destination. In particular, the findings of the motivations and characteristics of various segments of mainland Chinese tourists to Macau under the new economic circumstance of the anti-corruption storm in mainland China and an attempt at economic diversification in Macau could be utilized in marketing and promotion strategies. These practices include promoting a positive image of this city with a diversity of existing and potential tourism resources, as a unique melting pot of eastern and western cultures, and as having diversified economics for a sustainable future.

In practice, measures on both marketing and supplying could be taken according to the current segmentation of this study. For example, two aspects of evaluation measures should be taken into account to attract the segments of freedom seekers and multi-purpose seekers. First, the aspects such as being a ‘cross-cultural town’, ‘a coastal town for relaxation’, and ‘a coastal town with a slow way of life’ should be strengthened in the promoting of Macau in mainland China. A new image as a leisure destination that provides full relaxation and freedom should be constructed. Second, more facilities, space and activities for leisure and culture should be provided in Macau, such as offering the tourists a closer connection to the new identity of the territory, working in the old areas linked to culture, art, music, local gastronomy, literature, the creation of new natural parks for leisure and outdoor environment, treating betting more as a recreational leisure activity (giving an impression of true leisure), without showing tourism as having (massive) crowds, thus creating a greater diversity of tourism in Macau, creating more local events for tourists to participate in to experience the local culture and lifestyle. Furthermore, the new identity as a ‘world center of tourism and leisure’ should be marketed in most popular Chinese social media platforms including WeChat, Weibo, DianPing, etc. Besides this, the idea of gambling for experiencing instead of winning could also be planted in the heads of these two segments, which will alleviate the challenge of the gambling industry. In addition, for the segment of the ‘fun and special interest in gambling seekers’, the consumption of leisure and culture could also be a factor to extend their stays in Macau. More leisure facilities and cultural actives can be considered for placement around the casinos to provide the special interest in gambling seekers more proximity to these.

6. Limitations and Future Research

This study has several limitations which should be pointed out. First, although Chinese tourists are the major source of tourism in Macau, the total number of international tourist arrivals has greatly increased. Future studies could use the segmentation method to group international tourists in Macau in terms of their travel motivations. Second, specific characteristics of the sample and destination may limit the generalization of our findings. Thus, the results of this study may not be generalized to other destinations or demographic groups. Third, multiple variables—such as expenditures, duration of study, conative loyalty, etc.—could be included in future studies.