Influences of airport service quality, satisfaction, and image on behavioral intention towards destination visit

ABSTRACT

An airport significantly facilitates passengers to travel domestically and internationally. It can also attract passengers to visit the region where it is located. The present research examines the influences of airport service quality, satisfaction, and airport image on behavioural intention towards visiting the destination country. The survey was conducted with 314 Cambodian outbound travellers. The research has employed Confirmatory Factor Analysis and Structural Equation Modelling to measure the relationship of service quality, satisfaction, image, and behavioural intention. The results show that satisfaction strongly affects behavioural intention towards visiting the destination country, while air service quality has a moderate effect, and airport image has no effect.

KEYWORDS:

• Airport service quality
• satisfaction
• airport image
• behavioural intention
• outbound traveller

1 Introduction

The airport can link the passengers’ impression with the region. It has probably been developed with the symbol of the city where it is located and is also the pathway of passengers to other destinations (Ariffin & Yahaya, 2013; Nghiêm-Phú & Suter, 2018). The airport contributes to the destination and tourist experience and is travellers’ first and last impression of travellers towards the destination (Wattanacharoensil et al., 2015). Some passengers could have the opportunity to visit the country during their transits. The passengers who transit at Changi Airport for a minimum of five hours are given an incentive tour to visit Singapore, while travellers in Dubai who travel via Emirate airlines are offered an incentive tour in Dubai (Lohmann et al., 2009), so the passengers may feel connected to the country when they travel through the country’s airport. The first and last experience in the airport could shape passengers’ impressions of any particular country (Ariffin & Yahaya, 2013; Chao et al., 2013; Martin-Cejas, 2006; Yeh & Kuo, 2003). The airport may make passengers think of the country the airport belongs to and play a critical role in tourists’ decision to visit again. For instance, visitors who get a good impression of McCarran International Airport will have a good impression of Las Vegas (Nghiêm-Phú & Suter, 2018), and first-time passengers’ image of a country is shaped by the country’s international airport (Jeon & Kim, 2012). Based on their airport experience, passengers mentally relate the airport with the destination by regarding the airport as the ambassador of the place, an interpretive location of the destination slogan and image, and an integral part of the tourist experience (Wattanacharoensil et al., 2017). As the place that directly provides quality services to tourists until exactly before their departure from a destination, an airport could completely change tourists’ perceptions of the destination, making them either positive or negative during their receiving of the airport’s services. Thus, the airport can be a marketing tool to promote the country as a destination to passengers, inviting them to return to the country, especially after they have experienced travel through the airport.

The airport provides a wide range of services for various stakeholders. The development of airlines, the influx of passengers, and the variety of products and services within the airport make the airport a commercial centre, which offers facilities and services with the assurance of reliability, safety, efficiency, and pleasure during passengers’ experience (Goetz, 2019). Airports could be considered a type of shopping haven with the availability of trendy and tax-free products (Crawford & Melewar, 2003) and a primary shopping and retail market where passengers can purchase food and beverages, clothes, souvenirs, and other products and services (Omar, 2008; Omar & Kent, 2001). For instance, Hong Kong International Airport is regarded as a shopping and entertainment area (Tang et al., 2017). Additionally, the airport could function as a place for visits or entertainment (Adey, 2008; Omar, 2008) and a leisure attraction (Frealthy & O’Connel, 1999), where passengers can enjoy themselves and relax before departure, after arrival, and during transit. Thus, it is a unique shopping place (Lin & Chen, 2013). The airport also serves the needs of numerous stakeholders, including airlines/other businesses and their employees, governmental and security units, and local communities (Frealthy & O’Connel, 1999; Jarach, 2001; Percoco, 2010).

Service quality, satisfaction, and image are essential for the airport. Service quality is considered to be one of the attributes that make the airport attractive and competitive (Pantouvakis & Renzi). More advanced airport infrastructure is attractive to passengers, and assessing their opinions on services could facilitate service quality improvement (Bellizzi et al., 2020). Overall airport service quality management must involve passenger satisfaction (Eklof & Westlum, 2002) because passenger satisfaction is the key to showing airport operational performance (Chao et al., 2013). Satisfaction is associated with service quality, which could affect an overall experience of a place or airport. In tourism, the perception of a tourist destination or country is shaped at the airport (Chao et al., 2013; Martin-Cejas, 2006). Satisfied passengers could plan to use the airport or visit the country where it is located. The relationships among these concepts have been widely studied in other industries, including the lodging industry in the USA (Olorunniwo et al., 2006), the recreation industry in the USA (Cole & Illum, 2006), the healthcare industry in South Africa (Boshoff & Gray, 2004), and the airline industry in Taiwan (Lee & Hwan, 2005).

Pabedinskaitė and Akstinaitė (2014) summarised various researchers’ studies on airport operations and services, including passengers’ expectations and experience, airport operational efficiency and productivity, and airport service quality. Other studies (e.g. Ariffin & Yahaya, 2013; Bogicevic et al., 2013; Chao et al., 2013; Lin & Chen, 2013; Batouei, Iranmanesh, Mustafa, Nikbin, & Ping, 2020; Nghiêm-Phú & Suter, 2018; Oña et al., 2018; Wattanacharoensil et al., 2015) addressed subjects such as satisfaction, intention to recommend and reuse, shopping, and airport image. However, the literature review suggests that there is a research gap in the prediction of the contribution of airport service quality, satisfaction, and image in the behavioural intention of the passengers to return to visit the origin country of the airport.

The current research aims to examine the influences of airport service quality, satisfaction with airport services, and airport image on Cambodian outbound travellers’ behavioural intention towards visiting the destination countries where they have used the countries’ airports. The research focuses on the Cambodian outbound travellers’ experiences of the airports where they arrive and leave, but it does not cover the transit-route airports. More specifically, the research focuses on two research questions: 1) Are there any relationships among service quality, satisfaction with airport services, and airport image? 2) What are the effects of airport service quality, airport image, and satisfaction with the airport services on the intention to visit the country’s destinations? A model of behavioural intention towards visiting destination countries from the influential factors of airport service quality, airport image, and satisfaction with airport services is constructed using confirmatory factor analysis (CFA) and structural equation modelling (SEM) based on the data from the traveller survey. The current research demonstrates the essential role of the airport in attracting passengers to visit the country’s destinations.

Examining the airport and tourist destination link is still limited in the literature. A study (Wattanacharoensil et al., 2017) examined the relationship between the airport experience of air travellers and the tourist destination; however, the study employed a qualitative approach and recommended that subsequent research use the quantitative method to strengthen the results. The current research uses the quantitative method, so it responds to the recommendation. In investigating the relationship between tourist satisfaction with airport services and destination revisit intention, Seetanah and Nunkoo (2018) treated airport services as a part of tourist destination services, while the current research focuses solely on airport services. Prentice and Kadan (2019) examined the association between airport service quality, satisfaction with airport service, and host city revisit intention. However, they measured service quality dimensions with other constructs, whereas the current research focuses on the overall airport service quality, meaning it uses CFA to construct the five dimensions of airport service quality into one construct for examining its impact. Furthermore, the current research predicts the behavioural intention to visit the country. Moreover, the current research includes an airport image to predict behavioural intention, which differs from previous studies.

There are other different focuses of the current research. The current research focuses on outbound travellers from only one nationality and airports in general in the various countries where Cambodian travellers have been, while the previous studies have focused on airports of a particular country and multi-nationality inbound travellers as samples. Pantouvakis and Renzi (2016) stated that the nature of the airport service quality is complex for passengers from different countries and cultures to evaluate. Wattanacharoensil et al. (2017) also suggested that subsequent research concerning airport experience should focus on categorisation because passengers from different countries can experience the airport differently. The current research focuses on the travellers of only one nation, so it responds to the recommendations, and it is also the new case study on Cambodian travellers, whose statistics have been seen increasing for the last decade. Moreover, Seetanah and Nunkoo (2018) stated that the revisit intention in their study was designed in a dichotomous variable and recommended that a future study measure it with the alternative Likert scale. The current research uses the 7-point Likert-scale variables.

The current research is divided into eight sections. They include an introduction, literature review, and hypotheses, along with the conceptual model, methods, results, discussion, conclusion, implication, and limitations with thoughts on future research.

2. Literature review

2.1 Theoretical foundation

The service quality gap model identifies the space between expected and perceived quality (V. A. Zeithaml et al., 2018). V. A. Zeithaml et al. (2018) also added that many organisations’ tasks are to close the service gap between customer expectation and perception. Service quality was one of the most crucial functions of the service quality gap mod (Cândido, 2005). Customer satisfaction with the service reflects the service quality level provided concerning three elements: physical environment, interaction, and outcome (V. A. Zeithaml et al., 2018). Researchers have developed the service quality gap model to close the gap between customer expectation and perception. The service quality gap focuses on the customer gap, such as not knowing customer expectations, not selecting the correct service design, not delivering service standards, and not matching performance to promise. Researchers have evaluated the passengers’ perception of airport service quality by listening to the customer’s ‘voice’ and using it as performance benchmarks (Fodness & Murray, 2007). Passengers’ satisfaction plays a prominent role in key performance indicators for the operation of an airport (Yeh & Kuo, 2003) because the satisfaction level signifies the gap size of what is given and what is received. Passengers judge service quality based on their perception of the technical outcome, overall process, and the physical surrounding where the service was delivered (V. A. Zeithaml et al., 2018).

The theory of planned behaviour (TPB) extends the theory of reasoned action that made models limited in initially dealing with behaviours over which people have incomplete volitional control (Ajzen, 1991; Black, 2015). A crucial factor known as the individual’s intention from the TPB is formed to give the behaviour an intention as the indicator of willingness people have to try and how much effort they are planning to exert to perform a behaviour. The TPB initially regards intention as trying to the given behaviour performance rather than actual performance. Lam and Hsu (2004) stated that many earlier researchers have explained how the TPB predicts travellers’ intention towards choosing a travel destination. The theory has three independent conceptual determinants of intention: attitude towards the behaviour, subject norm, and perceived behavioural control. TPB suggests that the three elements are excellent predictors of intention and consequences of behaviour; however, the current research applies the theory to deeply explain the concept of behavioural intention towards country destination visit with the influences of other factors, namely service quality, satisfaction, and image, regardless of the three mentioned factors in the theory.

2.2 Behavioural intention

The behavioural intention, including an intention to visit and recommend, has been examined by many researchers. It is ‘the person’s subjective probability that he/she will perform in some behavior’ (Fishbein & Ajzen, 1975, p. 288). The intention is the likelihood to act, and when there is an opportunity, intention results in behaviour (Tang et al., 2017). Hence, it is the action that a person intends to behave in a particular condition. For instance, in tourism, the stronger a visitor’s intention towards a destination, the more likely he/she visits the destination, so it is crucial to examine the visit intention (Lu et al., 2016). The accurate assessment of behavioural intention could predict the visitors’ actions that may occur (Habibi & Rasoolimanesh, 2020). Behavioural intention has been widely applied in the business environment to reflect how customers’ propensity and intention are engaged with a product or service. Moreover, the favour of behavioural intention is most likely formed by the positive perception of service quality, and it leads passengers’ behavioural intention to repurchase/reuse, engage in positive word of mouth, and endorse the products/services and price sensitively. Behavioural intention is recognised in the literature as a significant predictor of a service firm’s profitability (Anderson et al., 1994; Slater & Narver, 1995) and the essential goal for consumer marketing organisation because it is the core element of the firm’s sustainability (Wu et al., 2016). Behavioural intention can affect the cost and revenues of the firm since it can be turned into actual behaviour: a client can repurchase a product, visit a store again, or recommend the firm’s products or services to others. The behavioural intention in the current research refers to the likelihood that outbound Cambodian travellers will visit the country whose airport they have used.

2.3 Airport service quality

Service quality has been widely studied for various businesses, including the airport. The studies on service quality appear to focus on the service quality perception for a particular business and determine how the resulting behaviour is essential for that business (Liu & Lee, 2016). The definition of service quality revolves around comparing expectations about the service with perceptions of how the service was performed (Chao et al., 2013; Grönroos, 1984; Parasuraman et al., 1991, 1985, 1994). It is the customer’s perception and value judgment of a product or service (Yuon & Jang, 2008) or the total impression of service efficiency provided by the company (Park et al., 2004). In 1988, Parasuraman et al. introduced the SERVQUAL model for measuring service quality through five dimensions – reliability, assurance, tangibles, empathy, and responsiveness – with 22 measurement items. Reliability includes the physical facilities and the personnel’s appearance. Assurance refers to exact and dependable service performance. Tangibles are about how the staff help customers and offer prompt service. Empathy refers to the knowledge and ability of staff to supply the services that make customers trust and be confident. Responsiveness is about caring for individual customers’ experiences. V. A. Zeithaml et al. (2018) added that service quality is a focused evaluation that reflects the customers’ perception of these five dimensions. In contrast, Oña and Oña (2015), referring to Grönroos (1988) and Lehtinen and Lehtinen (1991), showed the three dimensions of service quality, including technical quality, which concerns the actual quality perceived by consumers; functional quality, which is related to the means through which the customer perceives the technical outcome; and image. However, most of the service quality dimensions of Parasuraman et al. (1988) are about functional quality (Oña & Oña, 2015). Regarding the airport, some studies have addressed service quality issues in the airport industry to analyze and develop an airport service quality measurement called ASQUAL (George et al., 2013).

The literature suggests a variety of dimensions for measuring airport service quality. Fodness and Murray (2007) pointed out three dimensions in the airport service quality expectation model. The dimentions include a function concerning the effective and convenient movement of passengers and the ability of the airport personnel to solve problems, interactions related to the quality resulting from the interaction between passengers and airport service providers, and discussion about how to turn the airport servicescape into activities that create a pleasant passenger experience (Lubbe et al., 2011). Yeh and Kuo (2003) evaluated the quality of passenger services of Asia-Pacific airports with six main attributes: comfort, processing time, convenience, staff courtesy, information visibility, and security. Then Chao et al. (2013) measured the airport service quality with ground transport, check-in services, departure security inspection, signs and information, and airport service and facilities. Next, Seetanah and Nunkoo (2018) categorised airport services into passenger core services, passenger support services, and visitor management services to measure airport services in their study. Meanwhile, Bezerra and Gomes (2015) and Park and Park (2018) suggested that airport service quality can be measured by such dimensions as facilities, check-in, servicescape, security, ambience, convenience, mobility, and price. The servicescape includes the signs, layout, and transfer amenities of the airport, while ambience includes acceptable noise, comfortable temperature, and smell of the airport. Although various researchers applied various terms and dimensions to measure the airport service quality, they still have overlapping meanings. As Yeh and Kuo (2003) stated, there is a consensus conceptualisation of service performance at airports, so attributes and dimensions are context-based applications. Bezerra and Gomes (2015) suggested that future works focus on integrated service quality by including dimensions related to airport service quality because they have many significant areas of overlap. In short, the measurement of airport service quality is done based on what Nghiêm-Phú and Suter (2018) mentioned as airport attributes.

Passengers play an essential role in assessing airport service quality. The service quality of the airport is measured through the perception of airport users (Francis et al., 2003; Yeh & Kuo, 2003). Users play an essential role in defining and evaluating the services of an airport (Lubbe et al., 2011), so surveying passengers is one of the most critical tools in studying airport services. As the primary purpose of service provision is to satisfy passengers, a passenger survey can be the best way to determine their satisfaction, which reflects the service quality accordingly. The present research measured airport service quality through the passengers’ perception based on the five dimensions suggested by Bezerra and Gomes (2015) and Park and Park (2018).

2.4 Satisfaction

Satisfaction is the result of the evaluation of services consumed. Satisfaction has been explained in numerous ways and environments with various evaluation methodologies (Gruca & Rego, 2005). Satisfaction can fall from one step to another during the consumption process. Customer perception of performance is gradually formed by comparing the prior expectation with the confirmed services or products (Bezerra & Gomes, 2015). After using the services, customers can judge the quality of service, and then they will be satisfied or dissatisfied with the services accordingly. After the confirmation and purchase, expectations distinguish between pre-purchase and post-purchase, and satisfaction with services/products exists (Kristenen et al., 1999; Thong et al., 2006). Customer satisfaction with a specific service is an evaluation between the provided service and the expected performance evaluation (Nimako, 2012). If the customers receive the services they expect, they are satisfied. Thus, satisfaction is the evaluation of a particular service encounter (Park et al., 2004).

Like in other industries, passenger satisfaction is essential for airport service quality performance. It is the core performance indicator of the airport operation (Pantouvakis & Renzi,) and is related to the passengers’ perception of the service quality and servicescape (Bezerra & Gomes, 2015). Airports and airlines acknowledge the necessity of studying air traveller satisfaction and identifying the service quality indicators for improving travel experience (Bogicevic et al., 2013). The current research measures passenger satisfaction based on the perception evaluation that the passengers have on the airport services they have used.

2.5 Airport image

Image has been studied widely in business categories concerning customer perception. ‘Images range from holistic, general impressions to very elaborate evaluations of products, brands, stores or companies’ (Poiesz, 1989, p. 463). Image is the holistic means that customers define a business organisation in their mind (Pantouvakis & Renzi, 2016). It is mentally constructed based on some commonly determined impressions with the amount of total impression, and the construct is formed from the elements of various goods and attributes through the belief, ideas, and feeling on goods (Baloglu & Brinberg, 1997; Fakeye & Crompton, 1991; Mackay & Fesenmaier, 2000). When one person receives information on one object, the information will be processed to create the shape of the object in the brain so that he/she can describe it. If a passenger feels positive towards a particular airport, mainly when he/she receives the high-quality service, he/she can describe or remember it positively. Gartner (1994) introduced three components of the image. The cognitive image component refers to the assessment of the known products and attributes. The effective image component is associated with the motives of users. For example, users are motivated or decide to use products or services of a company because they are aware of the products or services. The conative image component is related to decisions or behaviour. With a cognitive image, the users could decide upon the information processed by the earlier two components.

An image is viewed from different perspectives. In tourism, the acknowledgement of the image of the destination is influenced by cognitive and affective evaluation (Baloglu & Mangaloglu, 2001). Image is the beginning point of tourist expectation that eventually determines tourist behaviour (Gu & Ryan, 2008; Nghiêm-Phú & Suter, 2018). From the perspective of consumer psychology, consumers cognitively acquire, process, and integrate information purchase decisions (Poiesz, 1989), and eventually, they judge the products or company. Moreover, corporate image (Gray & Balmer, 1998; Riel et al., 1998), brand image (Azoury et al., 2014), and business image (Madleňáková et al., 2019) are discussed in the literature. Meanwhile, the airport is seen as the first image of a destination for visitors (Ariffin & Yahaya, 2013; Martín-Cejas, 2005).

Airport attributes contribute to airport image. Airport image is formed from the passengers’ evaluation of the airport attributes (Nghiêm-Phú & Suter, 2018) or passengers’ overall impression of the airport (Park & Park, 2018). Some researchers (Ariffin & Yahaya, 2013; Goh et al., 2007; Jiang & Zhang, 2016) focus mainly on the physical attributes, such as ambient conditions, signs, symbols, and space function, making passengers more impressionable. Other researchers included the psychological attributes of the airport, such as courtesy of staff, efficiency/speed of check-in (Enoma & Allen, 2007), safety and security, service reasonableness, and service reliability (Barros & Tomber, 2007; Enoma & Allen, 2007). Nghiêm-Phú and Suter (2018) demonstrated the 11 studies of airport attributes by various researchers over more than a decade from 1992 to 2016. The attributes, to some extent, are similar or the same from one study to another and vary from one airport to another. However, those attributes are classified into physical and psychological attributes, and they represent the cognitive aspect of the airport image (Nghiêm-Phú & Suter, 2018). Additionally, Nghiêm-Phú and Suter (2018) mentioned the affective image, which results from feelings such as interest, relaxation or stress that passengers have towards the airport. Thus, these attributes can be the airport-based context. The present research operationalises the airport image as passengers’ impression of the airports’ services and facilities.

2.6 The relationship between airport service quality and satisfaction with airport service

Service quality is regarded as fundamental for customer satisfaction. Customers define the quality when using the goods or services supplied by the company, and satisfaction results accordingly (MBise & Tuninga, 2013). It means that satisfaction depends on quality. Some researchers (Iqbal et al., 2018; Lee et al., 2004; Oña & Oña, 2015; Parasuraman et al., 1985; Saravanan & Rao, 2007) have shown a direct relationship between service quality and customer satisfaction. Beyond the relationship, service quality influences satisfaction (V. A. Zeithaml et al., 2018). Service quality and customer satisfaction are the focused area of the airport industry and have a strong association (Bezerra & Gomes, 2015). Some studies (Chiou & Chen, 2012; Park et al., 2006; Rajaguru, 2016) in the airline industry found that the airline service quality positively affects passenger satisfaction, while other studies in the airport sector found satisfaction is associated with check-in, ambience, security, facilities, prices (Bezerra & Gomes, 2015), and facilities and servicescapes (Prentice & Kadan, 2019). Thus, the hypothesis (H) is stated that:

H1: Airport service quality has a positive effect on satisfaction with airport services.

2.7 The relationship between airport service quality and airport image

Service quality contributes to the image. The company wins favour in many aspects if they can create a positive image (Keller, 1993), and the corporate image relates to the firm’s overall service delivery, and service quality is an essential component of building a corporate brand image (Nguyen & Leblanc, 2001; Nguyen & LeBlanc, 1998). They suggested that the higher level of service quality reflects the higher level of an organisation’s brand image. The image is formed based on the evaluation of the services (Nghiêm-Phú & Suter, 2018). The airport attributes signify the quality of the airport services, which contributes to image formation. The positive or negative image of the corporate depends on the service quality the corporate offers to the customers. A study by Chou and Kim (2009) confirmed the positive relationship between customer perception of service quality and corporate image. Thus the airport image in passengers’ minds is associated with airport service attributes provided to the passengers. Then the hypothesis is stated:

H2: Airport service quality positively influences airport.

2.8 The relationship between airport service quality and behavioural intention towards destination visit

Numerous authors demonstrate the association between intention and service quality. V. Z. Zeithaml et al. (1996) stated that the higher the service quality, the more likely it affects the firm’s customer visit, reuse, or repurchase. When customers are satisfied with the service experience, they could be expected to recommend the service to their friends and relatives and be willing to revisit the same destination. In travel and tourism, it means passengers may have an intention to visit the destination. Chiou and Chen (2012) stressed that it is the decisive influential factor on behavioural intention, while Liu and Lee (2016) stated it indirectly relates to revisit intention. A study in the e-business (Elci et al., 2018) showed the essential of e-service quality for customer purchase retention. In the transport sector, Wang et al. (2020) found the relationship between service quality and intention to reuse rail transit, while Rajaguru (2016) found that airline service quality influences behavioural intention. The service quality of the airport appears to be essential to attract other good behaviour intentions (Tang et al., 2017), and airport performance contributes to the traveller’s anticipation towards the destination visit (Wattanacharoensil et al., 2017). Additionally, Prentice and Kadan (2019) showed that airport servicescapes are correlated with passengers’ intention to revisit the host city in Australia. Hence, the hypothesis is:

H3: Airport service quality positively influences behavioural intention towards destination visits.

2.9 The relationship between satisfaction with airport service and airport image

The literature shows the relationship between customer satisfaction and the image of the brand, product, or destination. According to Hu et al. (2009), the image of a service firm is affected by customer satisfaction, so the overall image is the consequence of customer satisfaction. Destination image and attribute satisfaction were both direct antecedents of overall satisfaction. In the hotel industry, hotel operators have improved the hotel industry’s physical environment and personal contact in communicating the hotel’s benefit, which directly creates perceived and significant positive images among customers (Nguyen, 2006). Airport image components, which include affective and cognitive components, are essential indicators of the passengers’ satisfaction (Nghiêm-Phú & Suter, 2018). It shows the association of satisfaction and the image of the airport. Some studies found that customer satisfaction positively affects bank image (Amin et al., 2013) and hotel image (Jani & Han, 2014). Thus, the hypothesis is:

H4: Satisfaction with airport services has a positive effect on airport.

2.10 The relationship between satisfaction with airport service and behavioural intention towards destination visit

The Literature shows that intention is associated with satisfaction. Satisfaction is not only associated with behavioural intention (Wu et al., 2016), but it is also a crucial predictor of behavioural intention (Moon et al., 2015). The intention that a visitor has towards visiting a destination is the willingness that he/she must visit the destination (Abubakar & Ilkan, 2016). Customer satisfaction denotes the overall pleasure and degree of satisfaction of customers. Satisfaction can motivate customers to repurchase a company’s product. It was found that tourists’ satisfaction level is related to behavioural intention (Bigne et al., 2001), indirectly enhances the visit intention (Oliver, 1993; Cronin, Taylor & Baker, 1994), and plays a vital role in making visitors revisit a place (Alexandris et al., 2006). Wu et al. (2016) also agreed that satisfaction is a defining factor in determining whether customers’ behavioural intention is to visit a destination or purchase a product. Assaker et al. (2011) and Seetanah and Nunkoo (2018) found that tourists’ satisfaction with airport services influences their repeat visit intentions. Some authors (Seetanah & Nunkoo, 2018) regard airport services as a part of destination services, so when travellers are satisfied with airport services, they might be partially satisfied with the destination’s services, which may eventually turn into an intention to visit the destination again. Hence, it is hypothesised that:

H5: Satisfaction with airport services has a positive effect on behavioural intention towards destination visits.

2.11 The relationship between airport image and behavioural intention towards destination visit

There is a relationship between image and behaviour (Alcaniz et al., 2005). When customers have positive images of products, services, or companies, they may repurchase the products or services from the companies. The same could happen for tourists or passengers who develop an excellent impression of a destination or an airport: they tend to revisit the destination or reuse the airport. The intention to return to the destination or to repurchase a product is behavioural, and a visitor with a more positive image is more likely to return to the destination (Bigne et al., 2001). As the first place where passengers arrive, the airport plays a vital role in shaping their impressions of the country (Ariffin & Yahaya, 2013), so with a positive overall image of the airport, passengers are more likely to visit the country again. The choice of the tourists to revisit a destination is related to how the attributes of the destination respond to their needs (Stylos et al., 2017), and airport service quality is a component of tourist satisfaction about a destination (Seetanah & Nunkoo, 2018). Passengers tend to revisit the destination if they are satisfied with the airport service. Assaker et al. (2011) found that a destination’s image influences behavioural intention to visit the destination, while Kim and Lee (2011) concluded that shop image had a positive effect on customers’ revisit intention. Consequently, a positive destination image would make a positive future behavioural intention (Chi & Qu,2008). Thus, it is hypothesized that:

H6: Airport image has a positive effect on behavioural intention towards destination visit.

2.12 Conceptual model

(Figure 1)

Figure 1. Conceptual model.

3 Methodology

3.1 Sample and sampling technique

The target population of the research was Cambodian outbound travellers who travelled to foreign countries from 2018 to 2020. The statistics of Cambodian outbound travellers have increased remarkably in the last decade. The increasing statistics appeared to be associated with the country’s Gross Domestic Products (GDP). GDP increased slightly from 7% in 2017 to 7.5% in 2018, but it slightly dropped to 7.1% in 2019, while outbound travelers rose from 1,995,499 in 2018 to 2,038,284 in 2019 (Ministry of Tourism, 2019).

The questionnaire survey was proposed to be conducted on 331 respondents, but the actual number of responses collected was 314 due to time constraints during the Covid-19 pandemic. However, 314 is still a large sample size for CFA- and SEM-based methods.

The research employed convenience and snowball sampling techniques because there was a lack of the name list of Cambodian travellers who have been to foreign countries. The majority of respondents were identified for the survey through the network, meaning that researchers asked the respondents if they knew others who travelled abroad; subsequent respondents were contacted for the survey accordingly. A smaller number were asked to participate in the survey based on their availability because the researchers knew they travelled abroad.

3.2 Measurement and data collection instrument

The questionnaire items were adopted from earlier studies. They included airport service quality, which was measured based on five dimensions, namely, facilities, check-in, servicescape, security, and ambience (Bezerra & Gomes, 2015; Park & Park, 2018); satisfaction (Bogicevic et al., 2017); airport image (Fodness & Murray, 2007); and behavioural intention (Moon et al., 2017; Omar & Kent, 2001; Tosun et al., 2015); (Table 1). The research used a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree) in the questionnaire. In addition, the questionnaire used both English and the Khmer language. Piloting was conducted to ensure questionnaire quality based on alpha values through a reliability test. Fifty respondents were selected for piloting. The results showed that the alpha values were 0.736 for facility, 0.771 for check-in, 0.705 for servicescape, and 0.770 for security. Furthermore, ambience got 0.698, security got 0.889, airport image received 0.796, and behavioural intention towards destination visit was 0.879. All alpha values fell in the acceptable ranges suggested by Leech et al. (2005), so the questionnaire items ensured internal consistency. The piloting results did not suggest the need to remove any items from the questionnaire, so all items were used in the final questionnaire.

Table 1. Sources of items

Codes	Items	Sources
ASQ	Airport service quality
FAC	Facility
FAC1	The airport provided comfortable and spacious seating around the terminal
FAC2	The airport provided aero-bridges that ease access from the terminal to aircraft
FAC3	Retail and dining option/restaurant offered a wide range of product
FAC4	The cleanliness of washroom/toilets are good
CHI	Check-in	Bezerra and Gomes (2015)
CHI1	The check-in process was efficiency
CHI2	Check-in staffs were helpful, friendly, and courteous	Park & Park, (2018)
CHI3	The self-check in kiosks were appropriated design
CHI4	Availability of luggage cart
SEC	Security
SEC1	I felt safe and secure during security screening
SEC2	security staff were helpful and, friendly and courteous
SEC3	Security screening was thorough
SEC4	Wait time at security check-point safe
AMB	Ambience
AMB1	The airport maintained clean facilities
AMB2	The temperature at the airport were comfortable
AMB3	The noise levels at this airport were acceptable
AMB4	The aroma at this airport was fitting
SERS	Servicescape
SERS1	The airport’s sign clearly directed me to service/facilities
SERS2	The airport’s layout was properly designed to cater for passengers’ special needs
SERS3	The airport physical layout avoided crowding and enabled easy movement
SERS4	Service in the transfer amenities was better than expected
SAT	Satisfaction
SAT1	I am happy with the experiences I had at this airport
SAT2	My choice to travel from this airport was a wise one	Bogicevic et al. (2017)
SAT3	I think that I did the right thing when I chose to travel from this airport	(2017)
SAT4	I have been satisfied with my experience at this airport
API	Airport image
API1	Terminal was clean	Fodness and Murray (2007)
API2	Toilet facilities were good
API3	Variety and Number of cafes and restaurants were available
API4	Interior design acceptable
BIDV	Behavioural intention towards destination visit
BIDV1	I intent to revisit that country in the near future	Moon et al. (2017)
BIDV 2	My experience at the airport made it more likely to visit that country again
BIDV 3	I am willing to visit that country	Omar and Kent (2001)
BIDV 4	That country will be my first choice over other destinations	Tosun et al. (2015)

The survey was done online and via paper-based means in June 2020. It was planned that the survey be done through face-to-face interviews. However, the online survey mode was applied to obtain the required number of respondents due to the Covid-19 pandemic in 2020 during the data collection. Due to the concerns regarding the quality of the online survey, though, the research balanced the paper-based and online surveys; that is, 50 % of the respondents filled in the questionnaire through the online form, while the other 50% were asked to fill in the paper-based questionnaire. The respondents were asked if they had travelled to a foreign country in the last three years from 2018 to 2020 to ensure they were in the research scope. Furthermore, the questionnaires were not distributed to those under 18 years old because they were not in the research scope. For the online survey, the questionnaire was designed in Microsoft Form, and the link was sent to the respondents through Facebook and Telegram.

3.3 Data analysis

The frequency was used to analyse the descriptive data of respondents’ profiles using the Statistical Package for the Social Science (SPSS) version 21. Confirmatory factor analysis (CFA) and Structural Equation Modelling (SEM) were applied for constructing models and testing hypotheses with AMOS Version 21. CFA was for constructing a measurement model. The ratio of Chi-square and the degree of freedom (X²/d.f) < 2.50, comparative fit index (CFI)> .95, normed fit index (NFI) > .95, Trucker Lewis index (TLI) > .90, the goodness of fit index (GFI) > .95, adjusted goodness of fit index (AGFI) > .95, and root mean square error of approximation (RMSEA) < .05 (Hair et al., 2014, 2019) were used to ensure the model fit. Furthermore, standardised loading > .70, the Average Variance Extracted (AVE) > .50, and Construct Reliability (CR) > .70 (Hair et al., 2014, 2019) were applied to ensure discriminant validity of the construct, while heterotrait-monotrait (HTMT) < .85 (Henseler et al., 2015) was calculated to measure the construct convergent validity of the model. SEM was conducted to build model and test hypotheses after the CFA-based measurement model indicated the perfect fit.

The data was checked for the common method bias. The current research employed Harman’s single factor testing by using exploratory factor analysis (EFA). If the result of one factor accounted for more than 50% of the variance, comment method bias would exist (Podsakoff et al., 2003). However, the result from the data of the current research shows a variance of 39.94%, ensuring the absence of common method bias.

4 Results

4.1 Respondent profiles

Table 2 shows the profiles of the respondents. The results show that about 60% of the respondents were female. The highest percentage (41.40%) comprised those 18 to 24 years old, and the second group (25–34) comprised 24.20%. Over half of the respondents (52.20%) were single, with 40.40% being married. The highest percentage (56.40%) held bachelor’s degrees, followed by those educated up to upper secondary school (22%). Also, 34% were students, followed by the employee group (26%).

Table 2. Profiles of respondents (N = 314)

Variables	Frequency	Percent
Gender
Male	130	41.40
Female	184	58.60
Total	314	100
Age
18–24	130	41.40
25–34	76	24.20
35–44	53	16.90
45–59	40	12.70
60 & above	15	4.80
Total	314	100
Single	164	52.20
Married	127	40.40
Divorced	18	5.70
Separated	5	1.60
Total	314	100
Education
Primary school	5	1.60
Lower secondary	11	3.50
Upper secondary	69	22.00
Bachelor’s degree	177	56.40
Master’s degree	43	13.70
Ph.D	9	2.90
Total	314	100
Occupation
Student	106	34
Employee	81	26
Entrepreneur	43	13.80
Teacher/Professor	25	8
Government officer	32	10.30
Unemployed	9	2.90
Retired	16	5.10
Total	312^a	100

a. 2 missing.

Table 3 illustrates the regions where respondents traveled in the three years from 2018 to 2020. The majority of the respondents (59.60%) traveled to Asian countries. Europe and Asia and the Pacific got the second and third places, accounting for 24.50% and 22.60%, respectively.

Table 3. Region of travel

Travel region^a	N	Percent	Percentage of cases
ASEAN	187	49.50	59.60
Asia and Pacific	71	18.80	22.60
Europe	77	20.40	24.50
America	17	4.50	5.40
Middle East	19	5.00	6.10
Africa	7	1.90	2.20
Total	378	100	120.40

a. Dichotomy group tabulated at value 1, multiple responses.

4.2 Measurement model

The CFA model shows the model fit indices. X² was 75.827, with a df value of 59, so the value of X²/df was 1.285. The results also showed GFI = .965, AGFI = .945, NFI = .970, CFI = .993, TLI = .991, and RMSEA = .030. Thus, the model was considered a perfect fit.

The factor loading value, AVE, and CR were determined to ensure convergent validity. The factor loading, AVE, and CR values were above the thresholds (Table 4). AVE exceeded .50, and CR was above .70, which is considered a high level of reliability. The items with factor loadings below .70 were removed to improve the model fit. Moreover, the HTMT value was calculated to ensure the convergent validity of the model constructs. The HTMT value was set at the maximum of .85. It was calculated using the correlation matrix value of the model items. The values of constructs fall below this threshold (Table 5). Therefore, convergent validity and discriminant validity were established.

Table 4. Convergent validity and reliability test

Constructs	Standardized Factor loading	Error Variance	AVE	CR
Airport service quality (SQ)
FAC	.787	.031
CHI	.862	.025
SERS	.859	.026	.687	.916
SEC	.830	.029
AMB	.804	.031
Satisfaction (SAT)
SAT3	.712	.056
SAT2	.809	.051	.583	.807
SAT1	.768	.062
Airport image (API)
API2	.686	.147
API1	.900	.168	.640	.695
Behavioral intention towards destination country visit (BIDV)
BIDV3	.771	.062
BIDV2	.800	.056	.622	.754
BIDV1	.795	.050

Table 5. HTMT result

Constructs	SQ	SAT	API	BIDV
SQ
SAT	0.84
API	0.49	0.60
BIDV	0.79	0.23	0.39

4.3 Structure equation modelling

SEM was employed to test the hypotheses after CFA. The same goodness-of-fit criteria of CFA were applied. The results demonstrate X^2/d.f = 1.285, GFI = .965, AGFI = .945, NFI = .970, CFI = .993, TLI = .991, and RMSEA = .030. Hence, the model has the perfect fit (Figure 2).

Figure 2. Estimate model.

Table 6 shows the SEM results of the hypothesis testing. The results include t-values, p-value, direct effect, and the criteria of hypothesis testing. Four hypotheses (H1, H3, H4, and H5) were accepted (p < .05), while two (H2 and H6) were rejected (p > .05). Airport service quality has a significant positive effect on satisfaction with airport service (β = .84, t = 11.637) and behavioural intention towards destination visit (β = .31, t = 3.736), but it did not affect airport image (p > .05). Satisfaction with airport service influenced airport image (β = .32, t = 2.119) and behavioural intention towards destination visit (β = .55, t = 4.401). However, airport image did not affect behavioural intention towards destination visit (p > .05).

Table 6. Hypotheses relationship testing

Hypotheses				Effect	t-value	p-value	Hypothesis testing
H1:	Airport service quality	→	Satisfaction	.84	11.637	***	Accepted
H2:	Airport service quality	→	Airport image	.22	1.534	.122	Rejected
H3:	Airport service quality	→	BIDV	.31	2.736	**	Accepted
H4:	Satisfaction	→	Airport image	.32	2.119	*	Accepted
H5:	Satisfaction	→	BIDV	.55	4.401	***	Accepted
H6:	Airport image	→	BIDV	.05	.858	.391	Rejected

*, ** and *** indicate statistically significant at p < .05, p < .01, and p < .001.

5 Discussion

The results show significant and nonsignificant relationships among airport service quality, satisfaction with airport service, airport image, and behavioural intention towards country destination visits. First, airport service quality, which includes facilities, check-in, servicescape, security, and ambience, has a large positive effect on satisfaction. It could be inferred that the higher service quality the airport offers, the higher satisfaction level the passengers have with the airport services. The result responds to the authors (Chiou & Chen, 2012; Iqbal et al., 2018), who states the relationship between service quality and satisfaction. Specifically, the result is supportive of Bezerra and Gomes (2015) and Prentice and Kadan (2019), who found that the stronger satisfaction of passengers is associated with airport check-in, basic facilities, security, ambience, and servicescapes. It is likely that the higher service quality the airport performs, the higher satisfaction the passengers have with the airport.

Airport service quality has a medium positive effect on behavioural intention towards destination visits. The result confirms the assertions of Liu and Lee (2016) and Rajaguru (2016) that passengers may return to visit destinations if the service quality is improved. The result is also in line with earlier studies that found a relationship between airport servicescapes and host city revisit intention (Prentice & Kadan, 2019). Hence, the result could indicate that passengers may visit the destination country if they experience the high-quality services of the airport in that destination country.

In contrast, airport service quality does not affect airport image. The result is different from the research findings that the service quality is associated with the corporate image (Chou & Kim, 2009; Lin, 2013; Nguyen & Leblanc, 2001; Nguyen & LeBlanc, 1998). The result indicates that airport service quality may not contribute to the airport image; that is, however good the service quality the airport provides, it still may not help passengers form an image of the airport. The result supports the study of Giovanis et al. (2014), which does not find that technical quality contributes to corporate image. It may be because the passengers do not have more experience of the technical quality at the airport, so it could be hard for them to evaluate the quality when forming their impression of the airport. However, the airport service quality may affect airport image indirectly through satisfaction as a medicator; that is, the passengers may have a good impression of the airport if they are satisfied with the airport services.

Satisfaction affects airport image and behavioural intention towards country destinations. Satisfaction has a medium positive effect on airport image. The result is similar to the studies (Amin et al., 2013; Jani & Han, 2014; Nghiêm-Phú & Suter, 2018) in the context of banks and hotels. Thus, it is likely that if passengers are satisfied with the airport services, they shape the positive image of the airport. More significantly, satisfaction has a large positive effect on behavioural intention towards visiting the country of destination. The result could be similar to that of previous studies (Assaker et al., 2011; Seetanah & Nunkoo, 2018), which state that customer satisfaction is correlated with revisit intention. It is supportive of Abubakar and Ilkan (2016), who said that satisfaction is a determinant of behavioural intention. Hence, the passengers who are satisfied with airport services may visit the origin country of the airport in the future, and the more satisfied they are, the more likely they will return to visit.

Airport image does not influence behavioural intention to visit the destination country. The result is inconsistent with that of earlier studies (Alcaniz et al., 2005; Ariffin & Yahaya, 2013; Assaker et al., 2011; Bigne et al., 2001), which found that image could make tourists revisit the destination. The opposite result might be due to the different contexts; the earlier studies determine the association of the destination’s image with a visit to that destination. In contrast, the current study measures the relationship between the airport image and the behavioural intention to visit the country of destination, that is, the current research tests how the image of one product can attract consumers to the associated product. The present research could be similar to the study of Blas and Carvajal-Trujillo (2014) that has not found a relationship between port image and intention to revisit the port. Therefore, it could be inferred that however good the airport image is, its image cannot attract passengers to visit its origin country.

6 Conclusion

The research examines the effects of airport service quality, satisfaction with airport service, and airport image on the behavioural intention towards country of destination visit. Furthermore, the research investigates the relationship amongst airport service quality, satisfaction with airport service, and airport image. The airport service quality is measured based on five dimensions: facilities, check-in, security, ambience, and servicescape. The results demonstrate both significant and nonsignificant relationships among the studied contracts.

Satisfaction with airport service has a large effect on behavioural intention towards visiting the country destination, while airport service quality has a medium effect on behavioural intention towards destination visit. In contrast, airport image does not influence the behavioural intention towards destination visits. Furthermore, airport service quality has a strong effect on satisfaction, yet it does not affect airport image, while satisfaction has a medium effect. Thus, airport service quality and especially satisfaction with the airport services play crucial roles in attracting airport passengers to visit the airport’s origin country as tourists regardless of the airport’s image. It proves the essential role of the airport service quality in attracting the passengers to return and that providing these services to satisfy passengers is more likely to make them return.

7 Theoretical and managerial implications

The most significant feature of the model is demonstrating the association of the airport with its origin county destination. The model examines how the airport service quality, satisfaction, and airport image draw the passengers’ attention to the country they experience at that country’s airport. It is significantly unidentical of many earlier studies that assess the intention to revisit the destination or repurchase the same product or service they have used. The current model measures how the service quality, satisfaction, and image of a product influence the customers’ intention to another associated product, i.e. the airport can attract its satisfied passengers to visit its origin country. It is like the term Lohmann et al. (2009) called airport turns passengers into tourists. Thus, the present research significantly contributes to the existing literature gap on which the earlier studies have not solely focused. The contribution of airport service quality and satisfaction with airport service quality to attracting passengers to visit the origin country of the airport is significant in the existing literature review.

The model indicates that satisfaction is the most significant influential factor on destination country visit intention. However, service quality increases passengers’ satisfaction, whereas airport image is not essential for drawing the passenger’s intention towards the country. Meanwhile, the model does not show whether airport service quality contributes to the airport image. It may require satisfaction as a mediating variable as airport service quality positively affects satisfaction, while satisfaction positively influences airport image. Therefore, the model suggests that improving airport service quality and making passengers satisfied with services potentially attract more tourists. The airport service quality in this model is measured based on five dimensions: facilities, check-in, security, ambience, and servicescape, as adopted from Bezerra and Gomes (2015) and Park and Park (2018). Hence, the model does not only confirm the studies of these authors but also suggests that subsequent research related to airport service quality can employ these five dimensions.

Furthermore, the current research confirms some parts of the earlier studies. First, the current research uses the seven-point Likert scale to measure behavioural intention in response to Seetanah and Nunkoo’s (2018) study that recommends that future research use the Likert scale rather than the dichotomous one, and the result shows that intention has a relationship with satisfaction with airport service quality. It can be inferred that the relationship between the two variables exists, regardless of the type of variable that was designed, so it contributes to the literature in that subsequent research can use either dichotomous or Likert-scale variables of intention to test the relationship with satisfaction, particularly in the context of the relationship between the tourist destination and airport. Second, the current research could partially contribute to the validity of the studies on airport service quality, satisfaction, and destination revisit intention. Prentice and Kadan (2019) and Seetanah and Nunkoo (2018) focus on airports, in particular, countries with multi-nationality inbound travellers to measure the relationship amongst airport service quality, satisfaction, and revisit intention, whereas the current research focuses on the outbound travellers of one nationality to various airports in Asia and the Pacific, Europe, America, and Africa to examine the relationship between airport service quality, satisfaction, and behavioural intention towards country destination revisit. The results of the current research support the two studies, so it can be inferred that the relationship amongst the variables exists regardless of different nationalities and airports.

Moreover, the current research indicates that satisfaction with airport service contributes to the airport image, which the previous studies have not emphasised. Thus, it is another significant contribution that the current research adds to the existing literature on airport context.

The model demonstrates the impacts of airport service quality and satisfaction on behavioural intention towards country destination visits. It gives several suggestions applicable to airport management and tourist destination management. First, airport management should pay more attention to the service quality and passengers’ satisfaction because both can make the passengers return to the country as tourists. Improving service quality is crucial for the airport, yet providing quality services satisfying passengers is crucial. When the passengers return to be the tourists to visit the origin country of the airport located, they will reuse the services of the airport, so the airport increases profits accordingly. Airport management can pay attention to improving airport service quality based on the five dimensions employed in the current study, which include facilities, check-in, security, ambience, and servicescape. The model does not show the influence of service quality on airport image, but improving airport service quality will increase the satisfaction of passengers, and their satisfaction will contribute to shaping the airport image. Second, airport management and destination marketing organisations (DMO) can use an airport as a tourism destination promotion tool, especially airports with high service quality. They can promote airports, especially to the air travellers, to attract them to visit the origin country of the airport. Third, strong collaboration among the airport management, authorities responsible for tourism development and management, airlines, and DMO should be done to enforce the significant role of the airport in tourism destination development. The collaboration should focus more on improving airport service quality to increase the satisfaction of passengers and on using the airport to attract tourists.

8 Limitation and future research

The research has a few limitations. Firstly, the respondents are Cambodian outbound travellers who travelled to other countries between 2018 and 2020 and have been to the destination airports only, excluding transit airports. Hence, subsequent research should focus on inbound tourists because it can cover tourists from countries with more nationalities and experiences. Furthermore, future research should focus on inbound tourists’ revisit intention based on their perception of the airport. Secondly, because the research was done during the Covid-19 pandemic, the respondents’ concerns about the Covid-19 pandemic might have affected their intention while answering the survey questionnaires. Thus, future research after the Covid-19 pandemic should be conducted to compare the results. Subsequent research should also combine the current studied concepts with the perceived risks of the Covid-19 pandemic as moderator. Thirdly, the following research should include the perceived risk of Covid-19 as a moderator to identify if it moderates the relationship between service quality, satisfaction, and behavioural intention. Additionally, the research should focus on the improvement of the airport service quality during the Covid-19 pandemic. Fourthly, the current research did not include in its scope how foreign language proficiency and frequency of travels through airports influence the service quality, satisfaction, and behavioural intention. Thus, future research should emphasise applying foreign language proficiency and frequency of travels through the airport as mediators and moderators. Last, the current research used non-probability sampling, snowball, and convenience sampling techniques, although half of the data was collected online, which can affect the quality of the data.

Data availability of statement

The data that support the findings of this study are available from the corresponding author, sharing upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.