Travel arrangement as a moderator in image–satisfaction

Abstract

With more and more destinations relying on repeat travelers, the inclination of tourists to revisit some destinations has become a significant topic of study. Therefore, the reasons for travelers’ revisits have been addressed in many research studies. These studies have determined several factors of the revisit inclination, such as satisfaction, destination image, and perceived quality. However, in most of the previous studies about the relationships among the destination image and tourists’ satisfaction, as well as their behavioral intentions to the destination, the moderating variables were not considered. Consequently, we analyzed the moderator effects of certain characteristics of different travel arrangements on the theoretical relationship among the destination image, tourists’ satisfaction, and their behavioral intentions to the destination. This article first identifies the similarities and differences among these two types of Chinese outbound travelers in terms of their demographic and trip characteristics. It then confirms that these two types of travelers differ in terms of the relationships among perceived destination images, satisfaction level, and future behavioral intentions using an invariance test of structural model. According to the multiple group cause-and-effect analysis, the results show that travel arrangement can largely affect either the relations between destination image and tourists’ satisfaction or their behavioral intentions. Moreover, the relationship between either destination image and tourists’ satisfaction or their behavioral intentions is generally stronger for independent visitors. Finally, the influences are discussed from both theoretical and practical perspective.

Keywords

Behavioral intentions destination image moderator effect overall satisfaction travel arrangement

Introduction

With the increment of personal disposable incomes and increasingly relaxed limitations to outbound travel, Mainland Chinese visitors have become one of the more important markets in outbound tourisms. In the last decade, the quantity of Mainland Chinese outbound tourists (hereinafter referred to as MCOTs) booms at the average annual growth rate of roughly 20% and reached 83 million in 2012 (UNWTO, 2013). Moreover, with the expenditure of nearly US$129 billion in 2013, MCOTs continue to be the largest market. Consequently, many outbound destinations are trying to attract MCOTs (Ali, 2013). To promote and satisfy MCOTs effectively, it is important to understand different types of Chinese outbound travelers (Euromonitor International, 2011). Despite increasingly strong international interest in Chinese outbound travelers, the Chinese market, and the importance of understanding the different types of MCOTs, current research has not adequately studied the commonalities and differences of these diverse types in terms of their characteristics, perceived images, satisfaction levels, and future behavioral intentions. Nevertheless, an important issue arises here, namely, how does the impact of image on overall satisfaction and behavioral intentions differ among various travel arrangement groups? As far as we know, no study exists on the possible differences between the two travel arrangements, the relationship between perceived images, satisfaction levels, and future behavioral intentions. So, it is necessary here to analyze the moderating effects of travel arrangement on the theoretical relationship with the destination image, tourists’ satisfaction, and their behavioral intentions. This study provides a first step for bridging this gap in the literature by studying MCOTs to Macau.

Chinese outbound travelers can be categorized according to the following different criteria: destinations (e.g. Europe, America, Africa, Australia, East Asia, Macau, Hong Kong, and Taiwan), traveling purpose (leisure or business), motivation (gambler or non-gambler), traveling arrangement (independent traveler, partial packaged travelers and all-inclusive packaged travelers), and region (East, West, Middle, etc). In this article, we categorize MCOTs according to their traveling arrangements. Because of the language obstacle, unfamiliarity, cost saving, and visa restrictions, some destinations can only be visited in groups by MCOTs. Most MCOTs had to join an all-inclusive packaged tour. However, more and more MCOTs have begun to travel individually due to a higher level of English, increased disposable income, ease of visa restrictions, and, most importantly, the freedom travelers enjoy when they do not have to follow a tour. This type of MCOTs normally travels individually or in small groups of two to four persons. Thus, they are able to arrange the trip according to their preferences. Different groups of MCOTs have different lifestyles and concerns (Liu and Li, 2012), and it is important to understand how they perceive a destination and how is perception impact on their satisfaction level and their behavioral intentions (Alcaniz et al., 2009; Assaker et al., 2011; Beerli and Martín, 2004).

It has been recognized that the destination image is an essential factor for success in tourism development, and it has become an important part of destination marketing strategies (Castro et al., 2007). In the last 10 years, the interest of both marketing practitioners and scholars on the ‘destination image’ has raised. Destination image is an important issue since overall satisfaction is dependent on image perception and the final goal of business operations is to influence behavioral intentions (Assaker et al., 2011). Suppliers in the travel and tourism industry are trying to create customer experiences that would generate positive perceptions while reducing potential negative perceptions. Similarly, customers, who purchase in the tourism industry, are more likely to choose destinations and attractions that both increase positive emotions and avoid negative ones. Therefore, the destination image mechanism becomes important for strategic issues regarding tourism business operations among which policy makers and academics are included.

Macau’s economy has been developing rapidly since transference back to China in 1999, especially since the casino market was liberalized in 2002. Macau’s international receipts totaled 51.6 billion dollars, causing it to be ranked number 5 in 2013 (UNWTO, 2013). Tourists travel to Macau for different reasons, but, for all intents and purposes, these can be categorized into gamblers and non-gamblers (Zeng et al., 2014) accordingly. Tourists, who admit of diverse characteristics, travel to Macau utilizing different travel arrangements (Liu and Li, 2012; Ong and Cros, 2011; So et al., 2011). It is important to understand the tourists to this destination in relation to their perceptions about Macau. We also hope to provide some managerial insights for effective promotion and service provision.

Literature review

Travel arrangement

The categorization of MCOTs according to traveling arrangement is not consistent and most researchers have provided overly subjective definitions. For the purpose of this article, we will follow the definition proposed by Hsieh et al. (1993). Hsieh defines independent travelers as adult Mainland Chinese citizens who make their own transportation and accommodation arrangements, choosing not to buy prearranged packages or tours. We will adapt the Wong and Lau (2001) definitions for packaged tours, that is, all-inclusive tours, which provide all ground activities (such as transportation, food, travel itinerary, guide service, and entertainment) and are sold at an all-inclusive price. Those travelers who purchased all-inclusive packages are called all-inclusive packaged travelers.

Previous research found that tourists’ selection of independent travel arrangement and the packaged travel arrangement is largely impacted by sociodemographic features, such as the age and gender of the tourists, travel characteristics, such as length of stay, size of the tourist party, and previous travel experience as well as nationalities and tourist destination (Bai et al., 2001; Hsieh et al., 1993; Mehmetoglu, 2006; Nishimura et al., 2007; Yamamoto and Gill, 1999). Several scholars compared packaged and independent tourists according to various dimensions including information searching behaviors, image perception, and activities undertaken. Due to the variations in travel arrangements, there are significant differences concerning travel information sources employed by these various tourists. A study carried out by Nishimura et al. (2007), for example, found that more freedom exercised by respondents during travel provided more chances to use guidebooks. Similarly, in terms of booking accommodation, trip length, activities undertaken, and size of the travel party. As noted by Yamamoto and Gill (1999), significant differences can be sensed between packaged travelers and non-packaged travelers. Despite the motives or expectations for independent travel, Japanese travelers highly depend on relevant traveling agencies that provide detailed traveling plans prior to the trip.

Several studies have been conducted to compare MCOTs’ characteristics and destination perceptions of different travel arrangements. For example, Law et al. (2008) compared the demographic, socioeconomic, and trip-related attributes of individual visit scheme (IVS) travelers and all-inclusive packaged travelers to Hong Kong. They surveyed 230 mainland Chinese travelers regarding the choice and evaluation of different restaurants. The results showed that the respondents generally considered these attributes to be important. However, the IVS travelers rated food quality and presentation significantly higher compared to the all-inclusive packaged travelers. Liu and Li (2012) did a further investigation about Macau’s destination image, as perceived by independent travelers, partial packaged travelers, and all-inclusive packaged travelers from Mainland China. A questionnaire survey’s empirical findings suggested that independent travelers had the best image perception for ‘gambling opportunities’, ‘hygiene of the environment’, ‘quality of tourism infrastructure’, ‘nightlife’, and ‘diversified products for shopping’. They had the worst image perception for transportation convenience, especially to tourism spots. From these cases mentioned, we can surmise that travel arrangement may also affect a destination’s perceived image. Further relevant studies are needed.

Destination image

Among the existing literature on tourism marketing, it is worth noting that perception is often explored in the form of image destination (Baloglu, 1999; Boo and Busser, 2006). Research on destination image traces back to the early 1970s with Hunt’s (1975) influential work examining the role of image in tourism development (Gallarza et al., 2002). The most commonly and broadly cited definition for destination image is the ‘individual’s mental representation of ideas, knowledge, beliefs, feelings, and global impressions about a particular destination’ (Beerli and Martín, 2004; Crompton, 1979; Gallarza et al., 2002). Much empirical research supports the premise that destination image is a multidimensional construct comprised of two closely interrelated components, namely, perceptive/cognitive and affective images (Baloglu, 1999; Baloglu and Brinberg, 1997; Baloglu and Mangaloglu, 2001). Here the term ‘cognitive image’ is referred to as the individual’s perceptions, ideas, beliefs, and knowledge about the tangible physical attributes of a place (Baloglu, 1999; Baloglu and Brinberg, 1997; Baloglu and Mangaloglu, 2001; Beerli and Martín, 2004; Castro et al., 2007; Chen and Hsu, 2000; Lee et al., 2005; Leisen, 2001; McCartney, 2008; Qu et al., 2011). Affective image refers to the feelings or emotional responses toward a destination (Baloglu, 1999). It is generally evaluated by the individual’s assessment of the following four aspects: (1) unpleasant to pleasant, (2) boring to exciting, (3) distressing to relaxing, and (4) gloomy to exciting (Baloglu, 2001; Baloglu and Mangaloglu, 2001; Beerli and Martín, 2004; Lin et al., 2007; Qu et al., 2011). In terms of the relationship between cognitive and affective image, most studies focus on how cognitive image affects affective image (Beerli and Martín, 2004). It is understandable that an individual’s emotional response stems from his/her knowledge of the objects (Russel, 1980).

The relationships between destination image, satisfaction, and behavioral intentions

Many studies have tried to build a complete model that includes destination image and its influences on visitors’ intended destinations and their satisfaction levels (Beerli and Martín, 2004; Chen and Tsai, 2007; Lee et al., 2005; Li and Yang, 2014). Numerous studies show a positive association between positive perceived image and satisfied tourists (Castro et al., 2007; Chi and Qu, 2008; Lee et al., 2005). A higher perceived image entails a higher level of satisfaction with the destination. For example, based on a questionnaire collected from visitors to a southern city of Spain, Castro et al. (2007) developed a model to evaluate whether destination image could greatly influence visitors’ satisfaction and, consequently, future behavioral intentions to visit certain destinations. In Lee et al.’s (2005) study, it was shown that different image dimensions affect visitor’s on-site experience differentially. For example, the exotic atmosphere dimension has no significant influence on visitor’s on-site experience. Chen and Tsai (2007) surveyed visitors in the Kengtin region, that is, Taiwan’s attractions, using a convenient sampling method. They arranged a ‘quality–satisfaction–behavioral intention’ model, observing perceived value and destination image as well as an entire visitor–behavior model. In their studies, destination image was shown to influence visitors’ behavioral intentions both in a direct and in an indirect way. The visitors’ future behavior was determined to be based on their level of satisfaction with their destination. From these, we can conclude that when the perceived image is better, the degree of satisfaction with the destination will be higher. Thus, it is more likely that visitors will evaluate this destination positively and be more likely to visit again (Castro et al., 2007).

Tourist satisfaction is a main determinant of tourist future behaviors. There is wide agreement among scholars concerning the significant positive correlation between tourist satisfaction and their intentions or behavior to visit/revisit the destinations (Beerli and Martín, 2004; Mansfeld, 1992; Heung and Qu, 2000). Furthermore, if a visitor is largely satisfied with a certain destination, he/she will likely wish to visit it again and be happy to recommend it to his/her friends and family (Oliver, 1980). When it comes to patterns of destination loyalty, Chi and Qu (2008) gathered empirical data in a major tourism destination in the state of Arkansas—Eureka Springs. They examined practical and theoretical evidence connected to the causal relationship among visitors’ perceived image, satisfaction, and behavioral intention. This study produced the following three results: (1) destination image could influence satisfaction with a destination’s attributes in a direct way, (2) both destination image and satisfaction with a destination’s attributes could make a direct contribution to overall customer satisfaction, and (3) satisfaction with specific attributes and overall satisfaction can affect the visitors’ behavioral intention in a direct and positive way.

Research model

From the literature review provided, we found that there is a positive relationship among perceived destination image, satisfaction, and behavioral intention. This has been confirmed for different destinations and tourists. Hence, three hypotheses, H1, H2, and H3, are hypothesized in this study too. The literature on travel arrangement further suggested that visitors of different travel arrangements have different characteristics and, consequently, different perceptions about these destinations. However, no study has compared the differences in relationships. Hence, we have provided a hypothesis (H4) that reflects the main objective of this article.

H1: Destination image has a significantly positive impact on tourist satisfaction.

H2: Destination image has a significantly positive impact on behavioral intentions.

H3: Tourist satisfaction has a significantly positive impact on behavioral intentions.

H4: The relationships among destination image, visitors’ satisfaction, and their behavioral intentions are different for independent and all-inclusive packaged travelers.

The research methodology

A questionnaire was developed to collect the necessary information. Although a questionnaire-based approach has some shortcomings, we used it in the text to conduct this type of relationship study due to its convenience. We also tried our best to reduce the errors in data collection. Prior to the formal survey, 25 respondents were invited to answer the questionnaire as a pilot survey to ensure clarity, reliability, and comprehensiveness. The questionnaire consists of two parts. The first part collected the trip information, namely, travel arrangement, past experience, purpose of the trip, and length of stay as well as demographic information, such as gender, age, education, and household monthly income. The second part of the questionnaire collected evaluations about destination image attributes, overall satisfaction level, and future behavioral intentions. The items included in the first part of the survey can be easily identified and the items in the second part are discussed subsequently.

Measurements

As noted by many researchers, it is important to dissect the individual components in the destination image construct (Chaudhary, 2000; Chen et al., 2009; Leisen, 2001). We developed the measurement items for Macau destination in two steps. First, we had an unstructured interview with three tourism professionals to identify the possible lists of the measurement items, these items were combined with the items we obtained from the literature (e.g. Chaudhary, 2000; Chen et al., 2009; Leisen, 2001; Li and Yang, 2014; Zhang and Chow, 2004). The complete list of items was then presented to 17 academics and industrial experts to seek their feedback regarding this list. Finally, the 17 items and 7-point Likert-type scale were applied to measure the destination image of Macau. Survey respondents were asked to indicate their level of agreement on the statements about each item in the list.

Overall satisfaction: Tourist satisfaction with the holiday experience was assessed with two items using a 5-point rating scale. The respondents were asked to choose a score to the question, ‘How would you describe your stay?’

Intention to return: Adopting the approach by Boulding et al. (1993), we used two items as the indicators of intention to return. The two questions on the respondents’ intention to return were, ‘How likely would you return to …?’ and ‘What is the possibility that you will visit…?’

Intention to recommend: The items here were also chosen based on existing literature (Boulding et al., 1993). The three items were ‘I will recommend Macau to my friends or family’, ‘I would say positive things about Macau to other people’, and ‘I would recommend Macau to those who ask me for advice’.

Sampling method

The respondents in this study were MCOTs at Macau from the age of 18 and above. After considering some difficulties such as limited time and man power, we chose to use a convenient sampling method. The respondents were sampled using an on-site intercept method. Four types of public sites where most of the Mainland Chinese travelers visit in Macau were selected for survey: (1) departure areas, (2) historical attractions, (3) shopping centers, and (4) Meetings, incentives, conferences, and exhibitions (MICE) centers. After excluding cases with missing values, a total of 835 respondents were retained for analysis.

Findings and discussion

Compare the profile distribution of independent and all-inclusive packaged travelers

We first conducted the χ ² test to compare the characteristics of two different types of MCOTs. The profile distribution of respondents is summarized in Table 1. Among the 835 participants, 451 were independent travelers and 384 were all-inclusive packaged travelers. We found that the two various types of MCOTs are significantly different with respect to almost all characteristics except gender. Apparently, independent travelers are younger than all-inclusive packaged travelers. Also higher educated travelers prefer to plan and arrange their own trips.

Table 1.

Demographic profile of respondents.

	Independent travelers (n = 451)		All-inclusive packaged travelers (n = 384)
	Frequencies	%	Frequencies	%	χ ²	df	Significance
Gender					0.054	1	0.817
Male	235	52.1	197	51.2
Female	216	47.9	187	48.8
Age					398.4	5	0.000^a
<20	51	11.3	5	1.3
20–29	184	40.8	26	6.8
30–39	131	29.1	24	6.2
40–49	42	9.3	58	15.1
50–59	29	6.4	131	34.1
60 or over	14	3.1	140	36.5
Education level					376.3	5	0.000^a
Pre-high school	22	4.9	122	31.8
High school	37	8.2	120	31.2
Vocational training	40	8.9	98	25.5
University	221	49.0	24	6.2
Postgraduate	113	25.1	14	3.6
Others	18	4.0	6	1.6
Past experience					347.8	1	0.000^a
First-time visit	136	30.2	360	93.8
Repeated visit	315	69.8	24	6.2
Length of stay (nights)					197.8	3	0.000^a
Less than 1 night	343	76.1	109	28.4
1–2 Nights	71	15.7	221	57.6
3–4 Nights	24	5.3	39	10.2
5 Nights or over	13	2.9	15	3.9

df: degrees of freedom.

^aSignificant at 0.05 level.

Regarding the length of time, the 835 participants stayed in Macau (Table 1), 23.9% of independent travelers’ visits lasted for more than 1 day, whereas 76.1% of them were day-trippers. For all-inclusive packaged travelers, only 28.4% of trips did not involve an overnight stay; whereas 57.6% involved 1–2 night stays, 10.2% involved 3–4 night stays, and 3.9% were 5 or more night stays. Apparently, most of the all-inclusive packaged travelers stayed in Macau for more than 1 day. Table 1 indicates that most independent travelers are repeat visitors.

Factor analysis on the destination images of Macau

To simplify the measurement of Macau destination image, we attempted to identify and validate the principal factor structure for the 17 items of image attributes. Exploratory factor analysis by principal component analysis was first used to detect the factor structure of the 17 items using eigenvalue above 1 as the selection criterion. Of the 17 image attributes in the original questionnaire, 2 were dropped from further analysis due to a low communality (less than 0.4), meaning a lack of correlation with other attributes in the scale. The Cronbach’s αs of each factor were between 0.801 and 0.889, showing a reasonable level of internal consistency among items. According to Table 2, the first dimension was labeled ‘cultural experience’, the second factor was ‘general and tourist infrastructure’, the third dimension was labeled ‘atmosphere of the place’, and the fourth dimension was labeled ‘tourist leisure and recreation’, which explained 66.29% of the variance.

Table 2.

Subdimension of destination image, the EFA results.

Image item	Factor loading				Commonality	Cronbach’s α
Factor 1: culture experience						0.851
Variety of interesting events and festivals	0.749				0.695
Unique European culture	0.807				0.686
Appealing local food and beverage	0.746				0.643
A lot of cultural attractions to visit	0.748				0.698
Factor 2: general and tourist infrastructure						0.818
Convenient transportation		0.773			0.735
Availability of tourist information		0.841			0.735
Good quality of tourism infrastructure		0.780			0.698
Ease access to tourist spots		0.806			0.740
Factor 3: atmosphere of the place						0.889
Relaxed atmosphere			0.767		0.705
Fashionable location			0.784		0.731
Exotic destination			0.820		0.781
Factor 4: tourist leisure and recreation						0.801
Great nightlife				0.714	0.643
Gambling opportunities				0.867	0.780
Diversified products for shopping				0.887	0.795
Variance explained (%): 66.29
Kaiser–Meyer–Olkin measure of sampling adequacy: 0.845
Bartlett’s test of sphericity: 3854.338
Significance: 0.000

EFA: exploratory factor analysis.

A confirmatory factor analysis (CFA) was applied to test the measurement structure of destination image. Table 3 presents the details of the properties of the measurements. The results indicated acceptable psychometric properties. For example, χ ² = 575.614, degrees of freedom (df) = 155; root mean square error of approximation (RMSEA) = .073; goodness-of-fit index (GFI) = 0.883; normed fit index (NFI) = 0.909; comparative fit index (CFI) = 0.931. Also convergent validity was assessed with the factor loadings in the measurement model. All confirmatory factor loadings exceeded 0.50, and all were significant at the α level of 0.05. Furthermore, average variance extracted (AVE) of all constructs ranged from 0.529 to 0.739, exceeding or approaching the recommended 0.5 threshold. Discriminant validity was also assessed by comparing the AVE with the squared correlations between constructs. Discriminant validity is justified if the variance extracted estimates for any pair of factors is greater than the square of the correlation between those two factors. In this study, the maximum squared correlation in all pairs (= 0.412 between natural and cultural resources and atmosphere) was less than AVEs (minimum value of 0.529), providing evidence of discriminant validity.

Table 3.

Structural model statistics (CFA).^a

Constructs and indicators	Mean scores (standard deviation); skewness; kurtosis	Completely standardized loadings	Composite and indicator reliability	Average variance extracted and error variance
Culture experience			0.852	0.591
Variety of interesting events and festivals	5.08 (0.887); −0.022; −0.158	0.800		0.360
Unique European culture	5.33 (0.809); −0.345; 0.116	0.730		0.467
Appealing local food and beverage	5.23 (0.879); −0.333; 0.880	0.759		0.424
A lot of cultural attractions to visit	4.72 (1.001); 0.558; 0.471	0.784		0.385
General and tourist infrastructure			0.815	0.529
Convenient transportation	5.21 (0.939); −0.596; −0.124	0.795		0.368
Availability of tourist information	5.45 (0.811); −0.029; −0.395	0.670		0.551
Good quality of tourism infrastructure	5.20 (0.864); 0.271; −0.183	0.584		0.659
Ease access to tourist spots	5.02 (0.875); −0.384; −0.053	0.832		0.308
Atmosphere of the place			0.889	0.729
Relaxed atmosphere	5.17 (0.968); 0.389; −0.515	0.830		0.311
Fashionable location	5.26 (0.964); 0.231; −0.443	0.875		0.234
Exotic destination	4.93 (1.050); 0.487; −0.226	0.856		0.267
Tourist leisure and recreation			0.810	0.589
Great nightlife	4.67 (1.241); 0.059; 0.506	0.682		0.535
Gambling opportunities	4.60 (1.152); −0.183; 0.368	0.844		0.288
Diversified products for shopping	4.73 (1.127); −0.791; 0.743	0.768		0.410
Overall satisfaction			0.854	0.661
I really enjoyed this trip	5.33 (0.960); −0.420; 0.005	0.834		0.304
My choice to visit Macau was a wise one	5.46 (1.104); −0.137; −0.873	0.828		0.314
This tourism experience in Macau was exactly what I expected	5.62 (0.936); −0.413; −0.161	0.776		0.398
Behavioral intentions			0.895	0.739
I plan to revisit Macau in the next two years	5.75 (1.082); −0.630; −0.034	0.827		0.316
I will recommend Macau to my friends or family	5.61 (1.173); −0.455; −0.619	0.892		0.204
I would say positive things about Macau to other people	5.97 (1.032); −0.910; 0.715	0.859		0.262

CFA: confirmatory factor analysis; df: degrees of freedom; CFI: comparative fit index; GFI: goodness-of-fit index; NFI: normed fit index; RMSEA: root mean square error of approximation.

^aMeasure of fit: χ²(df) = 575.614(155); χ²/df = 3.714; CFI = 0.931; GFI = 0.903; NFI = 0.909; RMSEA = 0.053.

Conceptual model and hypothesis

We used structural equation modeling (SEM) with maximum likelihood estimation to specify the conceptual model as shown in Figure 1. SEM enables an evaluation of how well the proposed conceptual model fits the data collected (Bollen, 1989). Therefore, the SEM procedure was deemed appropriate for testing the proposed theoretical model. A good model fit is typically inferred when the χ ²/df ratio falls below 5, when GFI and CFI rise above 0.90, and when RMSEA falls near 0.08. The model shows a relatively acceptable fit to the data according to the measures of absolute fit, except for the χ ² value, which may be affected by the sample size (see Figure 2). Most fit indices were either close to or exceeded the suggested criteria. Destination image can impact large affirmative influences on visitors’ satisfaction and their behavioral intentions (γ = 0.78, t value = 16.66, and p < 0.01; γ = 0.29, t value = 5.03, and p < 0.01, respectively), thus supporting H1 and H2. Finally, the testing outcome also proved the proposed assumption about the influence of visitors’ overall satisfaction on their behavioral intentions (γ = 0.65, t value = 10.37, and p < .01), which offers supportive evidence for H3.

Figure 1.

The conceptual model of the study with hypothesized paths.

Figure 2.

The relationships among destination image, overall satisfaction, and behavioral intentions. Note: IMG1–IMG4: culture experience, general and tourist infrastructure, atmosphere of the place, and tourist leisure and recreation. SAT1–SAT3: enjoyment, wise choice, and expectation. BI1–BI3: revisit, recommendation, and say positive things. Measures of fit: χ ²(df) = 57.811(32); χ ²/df = 1.807; CFI = 0.993; GFI = 0.977; AGFI = 0.960; NFI = 0.984; SRMR = 0.016; RMSEA = 0.040. df: degrees of freedom; CFI: comparative fit index; GFI: goodness-of-fit index; AGFI: adjusted goodness-of-fit index; NFI: normed fit index; SRMR: standardized root mean square residual; RMSEA: root mean square error of approximation.

Invariance test of measurement model

Before the metric invariance test, the study respondents were divided into independent travelers group and all-inclusive packaged group. As a result, the study samples in two groups were 451 and 384, respectively. Then, the equality between the factor loadings of both groups (measurement invariance) was examined. First, CFA was conducted for both groups without factor loadings, which was called as the non-constrained model; while another CFA was conducted for both groups with full factor loadings, which were called as full-metric invariance model. The above two different models were contrasted. Table 4 shows the measurement invariance test carried for the two groups of different travel arrangements groups. It was discovered that the χ ² difference between the model and full-metric invariance model is indifferent, χ ² (18) = 24.5, p = 0.139, which can be neglected. Consequently, it has been concluded that the changes caused by the different travel arrangement groups only have a slight impact on the measurement structure.

Table 4.

Measurement invariance test.

Groups	Models	χ ²	df	RMSEA	CFI	NFI	Δχ ²	Full-metric invariance
Travel arrangement	Non-restricted model	498.3	130	.049	.955	.974	Δχ ²(18) = 24.5	Supported
Travel arrangement	Full-metric invariance	522.8	148	.055	.943	.963	p = .139 > 0.01 (insignificant)

df: degrees of freedom; CFI: comparative fit index; NFI: normed fit index; RMSEA: root mean square error of approximation.

Invariance test of structural model

Some hypothetical paths are added between the research constructs to operate the SEM, so as to conduct the multi-sample analysis. In Table 5, the baseline models of the groups with different traveling arrangements are in line with the data (χ ² = 92.1, df = 62, CFI = 0.972, GFI = 0.945, RMSEA = 0.055, NFI = 0.951). We can assess the equality of a given path of the group with a different travel model by constraining the typical parameter to be sequentially equal. In particular, there is a contrast between the baseline models and other nested models with a χ ² difference test. For both of the two kinds of models, there is a certain parameter constraint in the two different groups. By this test, we were able to find the statistical significance of the differences in the parameter between two different travel arrangement groups. Table 5 shows the results of the invariance test and χ ² difference test. Travel arrangement significantly impacts the relation between destination image and tourists’ satisfaction, χ ²(1) = 4.6, p = 0.032. In other words, image perception can have a larger impact on independent travelers’ satisfaction (γ = 0.598, t value = 5.425, and p < 0.01) than all-inclusive packaged travelers’ satisfaction (γ = 0.514, t value = 5.001, and p < 0.01). Regarding the relation between destination image and behavioral intentions, the relations in the two groups were different, χ²(1) = 5.3, p = 0.021. For example, for independent travelers, their perceived destination image had a greater impact on their behavioral intentions (γ = 0.382, t value = 3.983, and p < 0.01) as compared to the all-inclusive packaged travelers (γ = 0.306, t value = 3.460, and p < 0.01). Regarding the relation between tourists’ satisfaction at the destination and behavioral intentions, both groups show a similar result, χ ²(1) = 2.4, p = 0.121. According to the findings of this research, travel arrangement does not impact moderating effects on the relation between tourists’ satisfaction to a destination and their behavioral intentions to the destination. As a result, the relation between these two constructs is the same in the two different groups and this does not vary from different travel arrangements. From the previous discussion, we may conclude that H4 only received partial support.

Table 5.

Invariance tests of the structural models for travel arrangement groups.

Paths	Independent travelers		All-inclusive packaged travelers		Baseline model (freely estimated)	Nested model (constrained to be equal)
	Coefficients	t Value	Coefficients	t Value	Baseline model (freely estimated)	Nested model (constrained to be equal)
DIM → SAT	0.598	5.425	0.514	5.001	χ ²(62) = 92.1	χ ²(63) = 96.7^a
DIM → BI	0.382	3.983	0.306	3.460	χ ²(62) = 92.1	χ ²(63) = 97.4^b
SAT → BI	0.476	5.201	0.447	5.006	χ ²(62) = 92.1	χ ²(63) = 94.5^c

Note: DIM = destination image; SAT = overall satisfaction; BI = behavioral intentions; CFI = comparative fit index; NFI = normed fit index; RMSEA = root mean square error of approximation; GFI: goodness-of-fit index; NFI: normed fit index; SRMR: standardized root mean square residual; RMSEA: root mean square error of approximation. Other goodness-of-fit indices of the baseline model for gender: CFI = 0.972; GFI = 0.945; RMSEA = 0.055; NFI = 0.951.

^aΔχ ²(1) = 4.6, p = 0.032 (significant).

^bΔχ ²(1) = 5.3, p = 0.021 (significant).

^cΔχ ²(1) = 2.4, p = 0.121 (insignificant).

Conclusions

Summary and managerial implications

Using data collected from MCOTs to Macau, we compared the characteristics of two different types of MCOTs and found some commonalities and differences among them. This could be helpful in segmentation of MCOTs. We reduced the dimension of destination image measurements significantly using data analysis tools. This enabled us to study the relationship among destination images, satisfaction level, and future behavioral intentions, applying the invariance test of a structural model. In line with the results of others’ research, our structural relationship analysis further suggests that destination image can affect not only visitors’ satisfaction but also visitors’ behavioral intentions. In particular, destination image seems to have a positive impact on visitor satisfaction. This means that visitors will have a higher possibility to be completely satisfied if their cognitive image of a destination is more positive. In addition, destination image does affect tourists’ behavioral intentions directly and indirectly in light of satisfaction. If the perceived image is more positive, the visitors will be more satisfied. This will make them want to visit a destination again as well as recommend it to others. These results were consistent with previous studies (e.g. Chen and Tsai, 2007; Chi and Qu, 2008). In order to promote the image of Macau, tourism managers should focus on the endogenous factors underlying the image of destinations, such as tourism infrastructure, tourist leisure activities, unique culture, and new attractions. Establishing or enhancing the image of a destination is beneficial to improve the overall evaluation and behaviors in the future, thereby promoting the success of the destination and boost the travel industry development (Assaker et al., 2011; Chi and Qu, 2008; Lee et al., 2005).

Although scholars have reached agreement about the relationships between destination image, tourists’ satisfaction at the destination, and their behavioral intentions, the moderating effects of travel arrangement on the relationships among these three factors have not yet been considered. Therefore, we hold that if the moderating effect is considered during the research design, the managerial relevance of research into tourist behavior can be greatly improved. Sometimes the travel arrangement adopted by a visitor can both affect and reflect his mood, value, and behavior. As a result, unlike previous studies, the research here has considered the moderating effect of travel arrangement on the relationships among these three factors. Through the multigroup cause-and-effect analysis, it has been found that the destination image has a stronger impact on both satisfaction and the behavioral intentions of independent travelers than on the satisfaction and behavioral intentions of all-inclusive packaged visitors. This might be due to the fact that the satisfaction levels and future behavioral intentions of the all-inclusive packaged visitors are affected not only by the destination image but also by the tour service. Hence, to improve the satisfaction level and obtain favorable behavioral intentions for this group, tourism management should strive to improve both destination images and tour services. For instance, tourism management could try to educate, regulate, and encourage the tour service providers to deliver good services effectively.

Limitations and future research

This study has several limitations. First, due to geographical proximity, the mainland visitors come predominantly from Guangdong province, which has a percentage of 66% traveling under the IVS travelers (Macau Statistics and Census Service, 2014). Although data have been collected from the four types of sites that most visitors frequented visit, the data collected may not be representative.

Second, this study did not distinguish gamblers from non-gamblers. However, Macau’s casino sector has enjoyed rapid expansion, especially in the area of targeting Mainland Chinese gamblers. There is a significant difference between gambling and non-gambling visitors. (Zeng et al., 2014). This deserves further investigation in as much as it would be important to know whether these two groups of visitors share the same destination image, satisfaction, and behavioral intentions. Furthermore, how the results of gambling, that is, loosing or winning, affects visitors’ destination perception about Macau would be a worthwhile topic to explore in the future.

Third, our study did not differentiate between shopping and non-shopping tours. Visitors with shopping motivation might perceive and behave differently from visitors without this motivation. Hence, it is important to study the differences between these two groups.

Fourth, our sampling method did not identify those ‘frequent border-crossing’ travelers, who, ideally, should be excluded from our research, since they are expected to behave differently from ‘normal travelers’.

Finally, only a single moderating factor was considered in this study. Many other factors have not yet been considered. For example, it would be worthwhile to investigate travel frequency, frequent border crossing, return visitors, motivation, shopping, and gambling as well as characteristics, such as gender, age, education, and family. All these factors might have some important moderating effects on the relations among the destination image, tourist satisfaction, and behavioral intentions.

Footnotes

Funding

This research was supported by University of Macau under research grant MYRG080 (Y2-L2)-FBA11-LXM.

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Travel arrangement as a moderator in image–satisfaction–behavior relations

Abstract

Keywords

Introduction

Literature review

Travel arrangement

Destination image

The relationships between destination image, satisfaction, and behavioral intentions

Research model

The research methodology

Measurements

Sampling method

Findings and discussion

Compare the profile distribution of independent and all-inclusive packaged travelers

Factor analysis on the destination images of Macau

Conceptual model and hypothesis

Invariance test of measurement model

Invariance test of structural model

Conclusions

Summary and managerial implications

Limitations and future research

Footnotes

Funding

References