Examining the moderating role of prior knowledge in the relationship between destination experiences and tourist satisfaction

Abstract

This study investigated the moderating role of tourist prior knowledge in the relationship between major destination experience components (i.e. attraction, infrastructure, restaurants and service performance) and tourist satisfaction. Data were collected from international tourists visiting Shiraz, Iran, via a questionnaire survey. Partial least squares structural equation modelling (PLS-SEM) was applied to analyse the data, and the multi-group comparison approach was adopted to test the moderating role of prior knowledge. Findings reveal that tourist prior knowledge moderated the effects of destination experience components on tourist satisfaction in a differentiated way. The effects of service performance and restaurants on tourist satisfaction were more significantly moderated by tourist prior knowledge than the effects of attraction and infrastructure on tourist satisfaction. This study suggests that destination experiences enabled by human performance are more important in contributing to tourist satisfaction for those tourists with little knowledge about the destination. Destination marketing implications are discussed.

Keywords

Destination experience PLS prior knowledge tourist satisfaction

Introduction

The tourism literature is fraught with studies comparing the behavioural patterns and differences between first-time and repeat visitors to a destination. It is commonly conceived that first-time visitors to a destination are more explorative, whilst repeat visitors tend to focus on fewer destination activities (Lau and McKercher, 2004; Oppermann, 1997); first-timers are more tourism- or sightseeing oriented, whilst repeat visitors tend to seek more recreation and relaxation activities (Li et al., 2008; Pyo et al., 1998). Studies also show that these two types of visitors are different in their perceived destination image, activity patterns and expenditure (Fakeye and Crompton, 1991; Kemperman et al., 2004; Meis et al., 1995; Oppermann, 1997). Despite numerous studies examining and noting behavioural differences of first-time and repeat visitors to a destination, no solid theoretical foundation has been applied except for that of the consumer involvement theory (Gursoy and Gavcar, 2003; Lehto et al., 2004). Nevertheless, this line of research suggests that tourists’ prior experience in a destination, or knowledge with a destination, plays a significant role in their behaviours towards the destination. For instance, Mazursky (1989) integrated past visitor experience into the customer satisfaction/dissatisfaction framework and empirically confirmed that whilst past experience did not influence satisfaction, it did have a significant impact on future intentions. In another study, Huang and Hsu (2009a) also confirmed that past visitations contributed to forming revisit intention.

Previous studies on the behavioural differences between first-time and repeat visitors and studies that directly examined the effect of past experience on repeat visitors’ behavioural intentions suggest that prior destination knowledge acquired through direct involvement and visitation experience influences tourist behaviours (Gursoy and Gavcar, 2003; Mazursky, 1989; Huang and Hsu, 2009a; Lehto et al., 2004). However, few studies have directly examined how prior destination knowledge functions in the relationship between on-site destination experience and tourist satisfaction. Understandably, direct involvement in the destination through past visitations constitutes only one source of destination knowledge (Baloglu, 2001); visitors’ individual levels of prior knowledge about a destination may vary substantially, especially among first-time visitors or prospective visitors (Awaritefe, 2004). Whilst prior research indicates that past visit experience could be a good proxy to prior knowledge in explaining tourist behaviours (e.g. Lehto et al., 2004; Huang and Hsu, 2009a), there is a need for more direct measurement and examination of prior destination knowledge in destination marketing studies (Tsaur et al., 2010).

In the tourism context, knowledge about a destination shapes tourists’ impression of what the destination can offer and, importantly, what can be experienced or consumed (Lew and McKercher, 2006; Ryan, 2000). Baloglu (2001) confirms that tourists’ familiarity with a destination, which can better represent their knowledge about the destination conceptually than prior visit experience, is positively related to their evaluations of cognitive, affective and overall destination image. With different levels of destination knowledge and experience, tourists demonstrate differentiated behavioural preference and involvements in destination activities (Lau and McKercher, 2004; Lehto et al., 2004). This clearly suggests that tourists with different destination knowledge levels may have varied evaluations on the destination attributes (Baloglu, 2001) and may be involved in destination activities in different ways (Lehto et al., 2004). Therefore, the level of satisfaction with various destination attributes and services could be moderated by tourists’ knowledge about the destination.

This study looks into the moderating role of prior knowledge on the relationships between different destination service and experience components and tourist satisfaction. Specifically, it aims to investigate:

how different destination experience components affect tourist satisfaction and

whether and how prior knowledge moderates the relationships between specific destination experience components and tourist satisfaction.

Literature review

Destination experience and tourist satisfaction

Tourism represents an experience-based industry (Pizam, 2010; Prentice et al., 1998). Experience is the main form of economic offering in tourism, and it is a more complex construct that is ‘inherently personal, existing only in the mind of an individual who has been engaged on an emotional, physical, intellectual, or even spiritual level’ (Pine and Gilmore, 1998: 99). In tourism and hospitality sectors, selling services is not enough to guarantee business success; business operators must be able to create their competitive advantage by creating and selling enjoyable, memorable and value-added experiences (Gilmore and Pine, 2002).

Tourism offerings and products are characterized by a combination of consumption experiences that involve multiple actors from both public and private sectors (Maunier and Camelis, 2013). It is important that destination marketing organizations focus on tourists’ destination experiences and linking tourism provisions and tourists’ desired experiences (King, 2002), as in most cases, tourist experience involves both the objective destination attributes (e.g. landscape and climate) and tourists’ subjective state of mind (Arnould and Price, 1993; Kim et al., 2012). Although researchers have attempted to measure tourist experience from the consumer’s perspective by conceptualizing and operationalizing tourists’ subjective states of mind (e.g. Kim et al., 2012), from a destination marketing perspective, it is more important to understand tourist experience and its associate concepts like tourist satisfaction based on tourists’ evaluations of destination provisions and attributes (Chen et al., 2011; Hsu, 2003). Therefore, the current study examines tourists’ destination experience by focusing on their evaluations of destination experience elements/attributes or components.

Using the critical incident technique, Maunier and Camelis (2013) identified a typology of tourism experience elements contributing to tourist satisfaction. Three categories of elements were identified. The elements related to the destination include natural factors, cultural factors, political and social–economic factors and urban policies; the elements related to services include transportation, accommodation, food, recreation/entertainment and rental services; and the elements related to human beings include host population, other tourists, personal social network and personal health. The results of Maunier and Camilis’s (2013) study suggest that ‘a broader holistic view of tourism experience is needed’ and ‘marketers should focus on elements leading to satisfaction and dissatisfaction’ (p. 19). Although Maunier and Camelis (2013) identified satisfying and dissatisfying experiential incidents in their study, the link between destination experiences and tourist (dis)satisfaction remains unclear.

In terms of destination experience components that may lead to tourist satisfaction, the literature does not inform a unified model that can be applied across different destinations and markets. In parallel with Maunier and Camilis’s (2013) effort, Buhalis (2000) identified six categories of destination components: attractions, accessibility, amenities, available package, activities and ancillary services. Comparatively, Kozak and Rimmngton (1998) argue that the components of a tourist destination can be classified into attractions, facilities and services, infrastructure, hospitality and cost. As destinations are all unique in their history, cultures, attraction types and industry infrastructure development levels, a unified model of evaluating destination attributes does not seem to exist in measuring tourists’ destination experiences in different countries. This study takes Iran as the study context; to our knowledge, there is little research on Iran’s destination attributes. As Butler et al. (2012) remarked, local input into identifying the unique selling points of a destination is an important element in the development of appropriate and culturally sensitive tourism in international tourism destinations like Iran. We therefore adopted an emic approach in identifying relevant destination experience components in the current study. The approach will be elaborated in the Methods section.

Consumer satisfaction generally concerns with the psychological state that is caused after transaction in a consumption context. In tourism studies, Oliver’s (1980) expectancy–disconfirmation framework has been one of the few theoretical foundations used in examining tourist satisfaction. Tourist satisfaction represents the overall evaluation of the psychological state derived from a cognitive–affective process (Bosque and Martin, 2008). It is thus resulted from a combination of the affective states in the process and tourists’ cognitive perceptions of the degree of fulfilment of their needs (Oliver, 1981).

Tourist satisfaction with a destination is determined by the experience a tourist obtained during the visitation. Hunt (1977) argued that satisfaction is the evaluation on the experience rather than the pleasurableness of the experience. This may represent more of the cognitive perspective in conceptualizing satisfaction. Indeed, with tourist experience in a destination, both personal intrinsic factors, such as states of mind and emotions, and external factors, such as perceived quality of service, may influence the formation of tourist satisfaction. Therefore, Huang and Hsu (2009b) argued that an individual’s overall satisfaction of a specific destination can be regarded as a subjective evaluation on the travel experience in the destination. We adopt this definition in the current article.

In measuring tourist satisfaction with a destination, the attribute-based approach has been widely employed (Alegre and Garau, 2011; Hsu, 2003; Huang et al., 2010; Kozak, 2003; Kozak and Rimmington, 2000; Pizam et al., 1978). In general, different studies generated different sets of destination attributes that affect tourist overall satisfaction with the destination (cf. Pizam et al., 1978; Kozak, 2003; Kozak and Rimmington, 2000). This demonstrates the atheoretical nature of the attribute-based approach, although it is useful to identify key destination attributes useful to market destinations.

The rising acknowledgement of experience and customer co-production in consumer research (Bendapudi and Leone, 2003; Berry et al., 2002; Gilmore and Pine, 2002; Ritchie et al., 2010; Yuan and Wu, 2008) has made the attribute-based approach increasingly challenged in studying tourist satisfaction with destination experience. Experience seems to involve both personal constructs such as emotions and destination physiques and servicescapes to affect customer satisfaction (Brunner-Sperdin et al., 2012; Coghlan and Pearce, 2010). Therefore, destination experience conceptualized as the major determinant of destination satisfaction should consider the connection and interaction between destination attributes and tourists’ psychological states.

Conceptualizing tourism experience has been one focus of tourism studies; however, there does not seem to be a commonly agreed-upon conceptual framework for tourism experience (cf. Larsen, 2007; Ryan, 2002; Tung and Ritchie, 2011; Uriely, 2005; Wang, 1999). Pine and Gilmore (1998) argue that experiences could be sorted out in four realms defined by two dimensions: customers’ active or passive participation and whether they are immersed or absorbed in experience scenarios. The four realms are entertainment, educational, escapist and aesthetic. They also postulate that a ‘sweet spot’ exists where all four realms meet and represent the ‘richest’ experiences (Pine and Gilmore, 1998: 102). Oh et al. (2007) empirically tested the applicability of the Pine and Gilmore (1998) model in tourism, and their results generally support the dimensional structure. Similarly, Kim et al. (2012) attempted to develop a scale measuring memorable tourism experiences with seven experience domains, including hedonism, refreshment, local culture, meaningfulness, knowledge, involvement and novelty. In another attempt to probe the essence of memorable tourism experiences, Tung and Ritchie (2011) identified affect, expectations, consequentiality and recollection as the four key dimensions of memorable experiences.

The complexity of tourism experiences lies in the fact that tourism experiences are generated with both the objective toured objects and the tourists’ subjective state of mind, meaning creation and interpretations (Huang, 2010; Martin, 2008; Uriely, 2005). From a destination marketing perspective, it is more relevant to examine how different levels and categories of destination attributes, services and performances affect tourism experiences. Whilst some destination features (e.g. physical environment and landscape) cannot be easily changed and thus seem less manageable, other factors, especially those in the industry supply system, are more malleable and could be well managed. Taking a marketing perspective, Mossberg (2007) argued that prominent factors making tourism experiences include physical environment, industry personnel, other tourists and products and souvenirs. Huang (2010) listed major destination experience enablers as environment (physical, atmospheric and weather), tourism supplies (hotel, attractions and transport) and people (personnel and other tourists).

Tourism experience involves services provided by multiple service sectors. Most prominently, accommodation, transportation, infrastructure, attractions and other customer care services (e.g. immigration) constitute the major components of destination experiences (Pizam et al., 1978; Kozak, 2003; Hsu et al., 2008; Song et al., 2012). These sectors should not be overlooked in evaluating tourist satisfaction with a destination. Song et al. (2012), in their effort on constructing the Hong Kong Tourist Satisfaction Index, identified six key service sectors (i.e. hotels, restaurants, retail shops, attractions, transportation and immigration services) in the destination to compose the overall tourist satisfaction with the destination experience.

In a tourist destination, the totality of tourism experiences may be created through tourists’ encounters with different types of experience cues. Berry et al. (2002) claim that organizations can manage the total customer experience by recognizing the cues that construct the customer experience. These cues can be classified into mechanics (functional cues) and humanics (emotional cues). Such a classification has its applicability in destination marketing. Certainly, some destination experience enablers (e.g. transport) appear to be more mechanical and functional, whilst others (e.g. personnel and service performance) will bear more human touch. Destination marketing practitioners need to understand the importance of different types of experience enablers to deliver satisfactory destination experiences.

The moderating role of prior knowledge

Tourist prior knowledge about the destination seems to function in the formation of tourist satisfaction. Lehto et al. (2004: 802) claim that ‘surprisingly, not much is known about how increased experience and knowledge influence behaviour at a destination’. Relevant empirical findings suggest it is unlikely that prior knowledge has a direct effect on tourist satisfaction with the destination (Huang and Hsu, 2009a; Mazursky, 1989). Furthermore, previous research suggests that a moderation role of prior knowledge in the formation of tourist satisfaction might be the case. For instance, Maestro et al. (2007), in their study on rural tourism in Spain, identified that destination familiarity, somewhat a proxy construct to prior destination knowledge, moderated the relationship between rural tourists’ attitudes toward rural tourism and their perceived service quality. To tourists who have more knowledge about the destination, their image and perceptions on the destination features are different compared to those who have little knowledge about the destination (Baloglu, 2001, Fakeye and Crompton, 1991); the differences in tourists’ image of the destination would affect their overall satisfaction levels (Chi and Qu, 2008). In the general consumer behaviour literature, Ellis and Ashbrook (1988) suggest that individuals with greater knowledge resist the interference of emotional states on their judgements more than those who possess less knowledge. In evaluating service satisfaction, Mattila (1998) found people with greater processing capability are less influenced by the state of mind. However, despite these distant clues in the literature, we have not found any published work examining the moderating role of prior knowledge on tourist satisfaction.

Conceptual model

Based on the literature review, we constructed our conceptual model (Figure 1). Considering the study context, we designated attraction, infrastructure, restaurants and service performance as four main domains that create destination experiences for international tourists to Iran (Shiraz). These four domains of destination experiences were designated on the basis of the literature (Buhalis, 2000; Kozak and Rimmington, 1998). These experience domains were proposed to exert different levels of effect on tourist satisfaction. Tourists’ prior destination knowledge was proposed to be the moderator in the model.

Methods

Measurements

The research presented in this article is part of a larger study investigating the various factors influencing foreign tourists’ satisfaction of visiting Shiraz, one of the famous tourist destinations in Iran. Based on literature review (e.g. Alegre and Cladera, 2009; Bosque and Martin, 2008; Bowen and Clarke, 2002; Buhalis, 2000; Kozak and Rimmington, 1998; Yuksel et al., 2009; Wang and Qu, 2006) and considering the nature of the destination, a questionnaire was constructed. The questionnaire and its items were reviewed for appropriateness by 12 professors and tourism professionals who are well known within the local tourism industry. This stage of expert judgement on the questionnaire items was especially important for the measurement of the four destination experience components. As aforementioned, no easily available scales in the literature could be adapted to the context of Iran and researchers suggested to engage with local inputs in identifying relevant measurement items in the Iranian tourism context (e.g. Butler et al., 2012). During the stage of expert evaluation on the questionnaire items, some questions were removed. For example, among those removed items, there was a question about the law against alcohol consumption. Although Shiraz is famous for its wines, the question was deemed problematic due to the general alcohol prohibition in Iran. The final survey instrument contained 42 questions, 13 for demographic information and travel characteristics and 29 questions to measure the variables of interest in the study.

For the purpose of this article, we selected relevant items measuring those salient destination experiences, including experiences with attraction, infrastructure, restaurants and service personnel performance. Specifically, attraction experiences were measured with eight items covering the uniqueness of the attractions, the perceived level of harmony of the attractions with their surrounding environments, artefacts and handicrafts at the attractions and meals and museums at the attractions; experience with tourism infrastructure was measured by tourists’ evaluations on public toilets, trash collecting facilities, accommodation and safety conditions. In most developing countries, public toilets for tourist use are one of the most discernible experiential clues for tourists to evaluate the tourist infrastructure in the destination. These items were regarded by the experts as appropriate in evaluating tourist destination experience with infrastructure. Experience with restaurants included the variety of food, availability of restaurants, taste of meals, price, cleanliness and service staff appearance. Experience with service personnel was measured by items evaluating the service behaviour of locals, tour guides, airplane crew, hotel staff and restaurant staff.

Tourist satisfaction was measured with three items, including items measuring the extent to which tourist expectation was met, tourists’ willingness to revisit the destination and the overall satisfaction with the trip. The first item was derived from the expectancy–disconfirmation theory (Oliver, 1980). The second item was chosen to measure satisfaction as an indirect measure verifying the external validity of satisfaction derived from the literature. Szymanski and Henard (2001: 24), in a meta-analysis on customer satisfaction, establish that there is a very high correlation (0.52) between satisfaction and consumers’ willingness for repeat consumption. Willingness for future consumption seems to be a good proxy for current consumption satisfaction. The third item is a direct measure for satisfaction with the trip. Consistent with most of the other questionnaire items, a five-point scale where 1 = not at all and 5 = very much was applied as the answer scheme. As a moderator variable, prior knowledge was measured with theoretical, technical and contextual considerations; specifically, two questions were used to measure the respondents’ level of prior knowledge about the destination: ‘How much did you know about Shiraz and its attractions prior to your trip?’ and ‘How much did you know about the customs and traditions in Iran prior to your trip?’. The scores of these two questions were averaged to identify a ‘high’ knowledge group and a ‘low’ knowledge group to test the moderating effects in the analysis.

Data collection and analysis

The survey was conducted at main hotels and attractions with international inbound tourists through convenience sampling in the city of Shiraz between February and September 2010. The questionnaire was designed in English and thoroughly checked for expression and accuracy before being administered for the survey. The survey was managed in a self-administered approach in which each respondent read the questionnaire and marked the responses by herself/himself. One researcher who is fluent in English went to all the data collection tourist venues and get the data collected. A total of 270 questionnaires were distributed to tourists who were confirmed to be at a late stage of their trips (the last quarter of their trip duration) so that they may have had sufficient destination experiences and were able to evaluate their satisfaction levels. Consent was obtained on-site, and the purpose of the survey and assistance were provided to the respondents.

At the end of the data collection, 266 completed questionnaires were collected. Both IBM SPSS version 22 and SmartPLS 2.0 M3 software were used to analyse the data. SmartPLS 2.0 M3 (Ringle et al., 2005) is a statistical software program used for partial least squares structural equation modelling (PLS-SEM) analysis. Compared with covariance-based structural equation modelling (CB-SEM), PLS-SEM is less restrictive in data requirements (e.g. normality of data distribution and sample size) (Assaker et al., 2012). In this study, as the sample size (N = 266) is relatively small, and it is expected by dividing the sample into groups for moderation analysis, the group size will be greatly reduced. This will render technical problems in data analysis when using a CB-SEM approach. However, PLS-SEM can effectively avoid these problems whilst ensuring the effectiveness of data analysis.

Sample characteristics were first generated with descriptive analysis in SPSS. Since PLS-SEM analysis depends on a full data matrix, it is important to scrutinize for any missing values. Sixty-six cases were identified as problematic and were removed because more than 10% of their values were missing. An additional 20 cases were identified with less than 10% of their values missing; these cases were retained in the analysis by replacing their missing values with imputed scores using the expectation–maximization estimation approach. Subsequently, 200 cases were used in the analyses.

Results

Description of the sample

The sample profile is summarized in Table 1. The majority of the respondents were from Europe (71.2%), and the average age was 51. A significant proportion of the respondents were retired (44.6%), married (61%) and perceived themselves earning an above-average level of income (53.6%). Whilst we do not claim the representativeness of our sample to the international inbound market to Iran, we should note that our sample may also be over-represented by those who are better educated, especially those with a doctoral education. In retrospect, this may be due to the fact that the hotels we chose for data collection were high-end hotels receiving foreign tourists, and many guests in the hotels were academics visiting Shiraz for conferences or other reasons. Table 1 also provides information on the travel characteristics of the respondents. The majority of the respondents were experienced international travellers (90.6%), but for many it was their first visit to Iran (86.1%) or Shiraz (91%). Most respondents (72.6%) stayed in Shiraz for 2 to 3 days, and almost half of them (52.8%) travelled with family and friends.

Table 1.

Profile of the sample.

Characteristic	Frequency	Percentage	Characteristic	Frequency	Percentage
Gender			Visits to other countries (in lifetime)
Male	141	53.8	<Five times	25	9.0
Female	125	46.8	>Five times	242	90.6
Residency			Visits to Iran
Asia	24	9.0	Once	230	86.1
Europe	190	71.2	Twice	15	5.6
Africa	5	1.9	>Two times	21	7.9
Americas	20	7.4
Oceania	27	10.1
Age			Visits to Shiraz
20–40	80	30.0	Once	243	91.0
41–60	86	32.2	Twice	8	3.0
61–80	100	37.5	>Two times	14	5.2
Education			Length of stay in Shiraz
No tertiary degree	72	27.0	<2 days	9	3.4
Bachelor	95	35.6	2 days	108	40.6
Master	61	22.8	3 days	85	32.0
PhD	38	14.2	4 days	50	18.8
			>4 days	14	5.3
Marital status
Married	163	61.0
Single	103	38.6
Occupation			Travel group
Student	35	13.1	Individual	29	10.9
Retired	119	44.6	Family	58	21.7
Employed	86	32.2	Friends	83	31.1
Other	14	5.2	Other	91	34.1
Self-perceived income level
Below average	8	3.4
Average	71	26.6
Above average	143	53.6
Well above average	41	15.4

Note: N = 266.

Descriptive and general PLS analyses

Table 2 summarizes the descriptive statistics for all the manifest variables. The results show that the respondents were pleased with their visit to the attractions (e.g. Persepolis, The Garden of Eram, Vakil Mosque and Kareem Khan Castle). However, the respondents were less pleased with the cleanliness and availability of public restrooms with a combined score below average (M = 2.66). The restaurants and general service performance of staff in various tourism sectors received above-average evaluations with a grand mean score of 3.99. Likewise, a grand mean score of 4.06 is received for overall tourist satisfaction, indicating that the respondents’ expectations were met and that they were willing to visit Shiraz in the future. The majority of indicators are above the midpoint of the scale and the related standard deviations are below 1.

Table 2.

Descriptive statistics, model specifications and bootstrapping results.

Factor/item	M	SD	SFL	t Value
Attraction	3.73
A1: I consider that the attractions and sceneries in Shiraz are unique	4.25	0.84	0.67	9.51
A2: The attractions are in harmony with its surrounding environment based on size	3.67	0.77	0.75	13.72
A3: The attractions are in harmony with the surrounding environment based on colours	3.70	0.82	0.76	14.19
A4: The attractions are in harmony with its surrounding environment based on materials	3.60	0.88	0.80	15.51
A5: The attractions are in harmony with its surrounding flora and fauna	3.70	0.89	0.75	12.00
A6: I am pleased with the artefacts and handicrafts at each attraction	3.64	0.86	0.59	6.90
A7: I am pleased with the meals at each attraction	3.58	0.98	0.57	5.87
A8: I am pleased with the related museums at each attraction	3.69	0.82	0.54	6.05
Infrastructure	3.29
I1: I am pleased with the availability of the public toilets and restrooms	2.86	0.97	0.47	2.89
I2: I am pleased with the cleanliness of the public toilets and restrooms	2.46	1.03	0.52	3.71
I3: I am satisfied with the trash collecting facilities	3.24	0.96	0.66	6.24
I4: I am pleased with my accommodation	3.93	0.84	0.75	11.03
I5: I am pleased with the safety conditions in Shiraz	3.96	0.96	0.72	8.18
Restaurants	3.63
R1: The variety of the food in Iranian restaurants is good	3.45	1.10	0.66	8.00
R2: I am satisfied with the variety and availability of the restaurants in Shiraz	3.43	0.90	0.70	9.75
R3: I am satisfied with the taste of the meals	3.69	0.94	0.82	22.18
R4: I am pleased with the prices at the restaurants in Shiraz	3.60	0.88	0.70	11.35
R5: The cleanliness of the restaurants is good	3.77	0.73	0.81	18.03
R6: The neatness of the waiting staff is good	3.82	0.78	0.78	15.53
Service performance	3.99
SP1: I am pleased with the service behaviour of the locals	4.37	0.88	0.85	24.62
SP2: I am pleased with the service behaviour of the tour guides	4.31	1.00	0.75	12.18
SP3: I am pleased with the service behaviour of the airplane crew	3.63	0.99	0.75	12.82
SP4: I am pleased with the service behaviour of the hotel staff	3.71	0.95	0.77	14.79
SP5: I am pleased with the service behaviour of the restaurant staff	3.62	0.90	0.73	12.23
SP6: I feel comfortable with the service behaviour of the locals	4.30	0.86	0.75	13.40
Satisfaction	4.06
S2: My expectations were met	4.13	0.82	0.92	45.46
S3: I am willing to revisit Shiraz in the future	3.79	0.98	0.80	15.25
S4: Overall, I am satisfied with my trip to Shiraz	4.26	0.83	0.93	76.78

M = mean; SD: standard deviation; SFL: standardized factor loading.

Note: n = 200. Five-point scale from 1 = not at all to 5 = very much.

Further PLS-SEM analysis was conducted for model testing. The PLS-SEM method estimates the relationships between the proposed latent variables in a path model. The estimation procedure uses a component-based approach that is comparable to principal components factor analysis (Compeau et al., 1999). The procedure focuses on estimating the structural path coefficients, which is represented by the inner model. The outer model represents the measurement model and specifies the relationship between the latent variables and their indicators (Henseler et al., 2009). PLS-SEM does not rely on distributional assumptions (Fornell et al., 1996). Therefore, direct inference statistical tests of model fit and model parameters are not available; as a solution to this, a global fit index and bootstrapping results with the t-values are provided (Chin, 2010).

Both the composite reliability and average variance extracted (AVE) scores are provided in Table 3 as measures for internal consistency and convergent validity. All measurement items had significant factor loadings, and the average factor loadings ranged from 0.62 for infrastructure to 0.88 for satisfaction. The majority of the loadings indicate that there was more shared variance between the construct and the measurement items than error variance (Hulland, 1999). The composite reliability scores ranged from 0.76 for infrastructure to 0.92 for satisfaction, thereby satisfying the minimum threshold of 0.6 (Bagozzi and Yi, 1988).

Table 3.

Reliability and discriminant validity.

Construct	CR	AVE	Attraction	Infrastructure	Restaurant	Satisfaction	Service performance
Attraction	0.87	0.47	(0.69)
Infrastructure	0.76	0.40	0.55	(0.63)
Restaurant	0.88	0.56	0.61	0.61	(0.75)
Satisfaction	0.92	0.79	0.66	0.60	0.68	(0.89)
Service performance	0.90	0.59	0.53	0.48	0.50	0.63	(0.77)

CR: composite reliability; AVE: average variance extracted.

Note: Square root of AVE is given in parentheses, and inter-construct correlations are shown off the diagonal.

The AVE measures the amount of variance captured by the construct relative to the amount of variance attributable to measurement error. AVEs should be no less than 0.50 to show convergent validity of the measurements (Fornell and Larcker, 1981). The AVEs for two of the latent variables – attractions and infrastructure – were slightly below the suggested minimum threshold. However, all of their factor loadings were significant; therefore, they were retained for further analysis.

Discriminant validity was assessed using the Fornell and Larcker criterion, which contrasts the squared correlation between two latent variables to the AVE scores for each of the two latent variables (Fornell and Larcker, 1981). Table 3 shows that discriminant validity has been achieved for all the associated latent variables since the squared correlation is smaller than both their related AVE scores.

Testing moderation effects

The original factor structure was retained to provide as much information as possible for the predictive analysis stage. Before testing moderation effects, the entire sample was submitted for analysis of the structural model. The results are presented in Table 4, and all of the structural path estimates are significant. The three strongest predictors of the respondents’ overall satisfaction are their evaluation of restaurants (β = 0.29/t = 4.94), attractions (β = 0.26/t = 4.18) and general service performance (β = 0.27/t = 5.43). These results are not unexpected since service performance and tourist satisfaction usually correlate positively. The respondents’ evaluation of infrastructure (β = 0.15/t = 2.58) was to a certain extent less important in contributing to their overall satisfaction. However, a better understanding could be achieved if we can pinpoint to the circumstances under which these relationships are stronger or weaker. The variation of the relationship refers to the identification and quantification of moderating effects. Moderating effects are evoked by variables whose variation influences the strength or the direction of a relationship between an exogenous and an endogenous variable (Baron and Kenny, 1986). Moderating effects indicate that the slope of the independent variable is no longer constant but depends linearly on the level of the moderator (Henseler and Fassott, 2010).

Table 4.

Path coefficients.

Relationship		t	p
Attraction → Satisfaction	0.26	4.18	0.00
Infrastructure → Satisfaction	0.15	2.58	0.01
Restaurant → Satisfaction	0.29	4.94	0.00
Service performance → Satisfaction	0.27	5.43	0.00

Note: n = 200.

We designated four destination experience factors (i.e. attraction, infrastructure, restaurants and service performance) as variables to predict tourist satisfaction and tourists’ prior destination knowledge was proposed to be the moderator of the relationships between destination experiences and tourist satisfaction. To test the moderating effect of prior destination knowledge on the formation of tourist satisfaction, we used a ‘multi-sample’ approach whereby the moderating variable is divided into two value categories. The group approach is similar to previous studies that used SEM to identify possible moderation effect (Homburg and Giering, 2001). However, to test the group differences statistically in this study, we compared path coefficients using a multiple group t-test for the path coefficients (Cohen et al., 2003). This method has been used by other scholars (Hsieh et al., 2008; Keil et al., 2013; Liu and Deng, 2015). Both prior destination knowledge questions were used to measure the moderator variable. A grand mean was calculated by averaging the means of the two question items. The grand mean (2.84) was then used to split the sample into two groups. Cases with a mean score of 2.84 or higher were classified into the ‘high’ group and those with a mean score below 2.84 were grouped into the ‘low’ group. There was almost an even split with 108 respondents for the high group and 92 in the low group. The path coefficients from all four destination experience factors to tourist satisfaction were estimated separately for both groups. Figure 2 shows the results for both groups.

All the structural relationships were found to be in the proposed positive direction, and the majority of the estimated path coefficients are significant. The path coefficients varied from 0.09 to 0.40 in the high group and from 0.13 to 0.42 in the low group; this demonstrates that different destination experience factors have varied effects on tourist satisfaction across the groups. The positive direction of the parameters can be interpreted as follows: for instance, a one-point increase in the evaluation of service performance will increase tourist satisfaction by 0.42 points for the low group (see the corresponding path coefficient in Figure 2). Accordingly, increased service performance means that tourists would be more satisfied with their overall experience. However, for the high group, the relationship between infrastructure and satisfaction was not significant. This suggests that tourists with a high level of knowledge of the destination may have more realistic expectation about the infrastructure of the destination so that they would not have any perceptual gap with regard to destination infrastructure to affect their overall satisfaction. Tourists who are well aware of the often dire state of public toilets in developing countries may not have such high expectations and adjust them accordingly without having an impact on their satisfaction levels. This is in line with previous destination image research (Baloglu, 2001; Chi and Qu, 2008). In contrast, for the low group, the relationship between attraction and satisfaction was not significant. This suggests that tourists who had less knowledge about the destination did not generate their satisfaction from their experience with attractions. As they know little about the destination, their satisfaction with the destination experience may be well balanced across perceived performance in various experience components. Tourist attractions’ effect on their satisfaction was limited. Although this finding is a bit out of expectation, it does indicate that prior knowledge of the destination played a differentiating role in generating tourist satisfaction among tourists. Whilst attractions did not have a significant impact in determining tourist satisfaction in the low-knowledge group, service performance did (β = 0.42, p < 0.10) and its impact was even greater than that reported (β = 0.17, p < 0.10) in the ‘high-knowledge’ group.

The model explains a considerable amount of the variance in tourist satisfaction for both groups. The R ²s ranged from 0.61 for the low group to 0.67 for the high group. These values indicate that at least 61% of the variance in satisfaction can be explained by the four predictors. Tenenhaus et al. (2005) introduced a global fit measure for PLS-SEM that has been widely accepted (Guenzi et al., 2009; Wetzels et al., 2009), and it is defined as the geometric mean of the average communality and the average R ², ranging between 0 and 1. Wetzels et al. (2009) specify 0.36 as the threshold value for a large effect size demonstrating good model performance. The goodness-of-fit value was 0.56 for the low group and 0.63 for the high group, both exceeding the threshold value of 0.36, confirming that the models performed well across both groups.

Examining moderating effects are important because theoretical relationships may vary under different conditions and contexts (Chin et al., 2003). The moderation effects are apparent due to the differences in parameter estimates when the same model is applied to different but related sets of data (Rigdon et al., 1998). The differences in the structural parameters between different groups can be interpreted as moderating effects (Henseler and Fassott, 2010). Table 5 compares the differences of the path coefficients between the two groups and indicates whether the differences are significant.

Table 5.

Moderation effects.

Relationship	High (n = 108)	Low (n = 92)	Difference	t	p
Attraction → Satisfaction	0.32	0.13	0.22	1.70	0.09
Infrastructure → Satisfaction	0.09	0.25	0.16	1.43	0.15
Restaurant → Satisfaction	0.40	0.15	0.25	2.28	0.02
Service performance → Satisfaction	0.17	0.42	0.25	2.17	0.03

Depending on the exploratory nature of the study, we chose the 90% confidence level in marking the statistical significance of the findings. At the 90% confidence level, the difference (0.25) between the path coefficients from service performance to satisfaction across the two groups, that (0.25) between the path coefficients from restaurant service to satisfaction and that (0.22) from attraction to satisfaction were statistically significant. The difference (0.16) between the path coefficients from infrastructure to satisfaction across the two groups was not significant (p = .15). These results suggest that there are significant moderation effects at the 90% confidence level (p < 0.10), with three of the four destination experience enablers.

Conclusions

This study examined the moderating effect of tourist prior knowledge on the relationship between destination experience and tourist satisfaction. The study was conducted in the context of international tourism in Iran. Data were collected from a questionnaire survey from 266 international tourists to Shiraz, Iran. PLS-SEM was applied to analyse the data and run the multi-group analysis to test the moderating effect of prior knowledge. Findings show that destination experience components (e.g. attraction, infrastructure, restaurants and service performance) affected tourist satisfaction. However, the effects of these destination experience components on tourist satisfaction were moderated by tourists’ prior knowledge of the destination. For tourists with less destination knowledge, experience with attractions did not seem to be a key determinant to tourist satisfaction, but destination services provided by various types of service personnel had a stronger effect. For tourists with more destination knowledge, experience with attractions significantly affected satisfaction, whilst experience with tourist infrastructure was not a key determinant factor. It may be speculated that tourists with more knowledge of the destination seek to verify their knowledge with the attractions they experience; such a mindful learning process may feed into their overall satisfaction evaluation with the destination experience. Tourist experience with restaurants in the destination appeared to be significant to both groups, whilst the effect of restaurants with the high-knowledge group was more salient. Service performance by destination contact service staff exerted a stronger effect on satisfaction of the low-knowledge group than it did on satisfaction of the high-knowledge group.

This study generates both theoretical and practical implications. Theoretically, it confirms the moderating role prior destination knowledge plays in the formation of tourist satisfaction, thereby enriching our understanding of the formation of tourist satisfaction with destination experience. As noted in the literature review, although tourist satisfaction has been a central interest in tourist/tourism studies, very little has been done to examine how prior knowledge about a destination fits into the formation of tourist satisfaction. The current study filled this void and at least offered some important clues to further understanding the issue. This study illuminates that the experience enabling areas that require more human interactions and services (e.g. service performance, restaurant and attraction) seem to be more important to create tourist satisfaction than those experience enabling areas that require less human service (e.g. infrastructure). This finding can be related to Berry et al.’s (2002) framework of humanics and mechanics in making customer experiences. The current study offers some evidence to show that humanics factors may be more important than mechanics in making tourist experiences in a destination, especially in a cross-cultural tourism context.

Practically, based on our findings, we suggest that industry practitioners take prior knowledge as a more workable tourist characteristic in destination marketing. Tour operators may need to solicit information about a tour participant’s prior knowledge level with the destination from the beginning of the service delivery in order to better design the experience components in an itinerary and ultimately making their clients more satisfied. To tourists who have a low level of knowledge about the destination, tour operators and destination managers should pay due attention to guarantee service performance in different destination service areas. To tourists who are more knowledgeable about the destination, attractions and restaurants must be chosen more carefully as they are more critical to determine tourist satisfaction. In a cross-cultural tourism context, tourists with a high level of destination knowledge may evaluate attraction presentations and ultimately form their satisfaction based on their prior knowledge; therefore, tourist knowledge should be taken in a more practical way in destination marketing, especially in market segmentation. Our research also discloses a possible overlooked area in tourism marketing; that is, tourism marketing is delivered to potential tourists without considering their prior knowledge of where they go. We argue that tourists should be treated in a differentiated way based on their prior destination knowledge from a marketing perspective.

This study has limitations. First, the sample size is relatively small; as a result, the statistical estimates may be either inflated or deflated (Royall, 1984). Second, the sample of this study, dominated by European tourists, may not be representative to international tourists from other regions and tourists from some emerging markets such as China and India. Therefore, the findings should be generalized with caution. Third, the destination experience areas in this study may not be inclusive. Fourth, tourist trip characteristics (e.g. leisure vs. business) may also moderate the relationship between destination experiences and satisfaction but was not tested in this study. Future studies may consider more inclusive destination experience conceptualizations and include trip characteristics in the model testing.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

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