Experienced international business traveller’s behaviour in Iran: A partial least squares path modelling analysis

Abstract

Few researchers have examined travellers’ experience with destinations despite the importance of their attitudes, behaviour and perception in selecting destinations. Current study aims to examine the relationship between risk perceptions, motivation, information source, travel experience and destination image among experienced international business travellers in Iran. The total number of 234 valid questionnaires was collected from international business travellers and structural equation modelling was employed using partial least squares path-modelling analysis to assess measurement and structural model for reflective constructs. Our empirical results support the negative relationship between destination image and risk perception, travel experience and risk perception while information sources were found to be unrelated to travellers risk perceptions. The results further shown that information sources and destination image, information sources and motivation, motivation and travel experience and destination image are related. However, the partial least squares-multigroup analysis results reveal that the significance of path coefficients differs across various demographic subgroups. Moreover, our results support experience and risk perception as a second-order reflective construct. Practical and theoretical implications are discussed along with a discussion on research limitations.

Keywords

International business travellers risk perception information sources motivation destination image partial least squares path modelling analysis

Introduction

Tourism is an important source of revenue in different countries (Chen, 2011; Kim et al., 2011; Law et al., 2006; Loureiro, 2014; Mwaura et al., 2013; Narangajavana and Hu, 2008) and continues to play a significant role in economic growth (Attallah, 2015). It has become a driving force for regional development and has embraced a growing number of new destinations (Kim et al., 2013b) resulting in rapid increasing trend in international travel. In 2011, 982 million travellers travelled internationally for the purpose of visiting relatives and friends or business activities and leisure (Wadhwaniya and Hyder, 2013). The number of travellers is expected to reach 1.6 billion by 2020 (Hartman et al., 2009; Huang, 2013; Li et al., 2012; Yang et al., 2010), and potential travellers have the choice to travel to more than 200 nations with over two million destinations (Jani and Hwang, 2011). For the purpose of this study, the phrase of traveller described as individuals who travel to a particular destination and stay there for less than a year (Behrens and Carroll, 2013; Cardon et al., 2011). These individuals can be grouped into two categories: leisure travellers or business travellers (Yang and Zhang, 2012). Business travel is fundamentally different from leisure travel, in part due to its non-discretionary nature (Ho and McKercher, 2012). Business travellers normally do not pay for their own flights (Lederman, 2007) ensuring companies maintain greater role in the control of business travel costs (Mason and Gray, 1999). More importantly, business travel is more stable compared to leisure travel as leisure travellers are sensitive to the political as well as economic changes in their decision-making process, while business travellers have insufficient choice whether to travel to a destination or not (Kucukusta et al., 2014).

The business travel sector has grown rapidly over the past several decades and has received a lot of attentions from academics and tourism practitioners (Dolnicar, 2002). According to Gustafson (2012), the financial worth of the business travel sector exceeded USD 800 billion worldwide in 2010. Considering the importance of business travellers and their financial impacts on the economic growth of destinations and the impacts of different factors on their decision-making processes, this study will try to examine the relationship between risk perceptions, motivation, information source (IS), travel experience and destination image among experienced international business travellers (IBTs). The paper will first present the overview of experienced IBTs followed by the literature review and hypotheses development. The subsequent section will discuss the research methodology and the analyses. The final section will provide the conclusion and limitations of the study.

IBTs

Travelling for business purposes is a significant component of international travel and is a key element in today’s global business relationships (Kulendran and Witt, 2003). For the purpose of this study, international business travel is defined as trip which is not open to choose by travellers and happen for the purpose of work-related activities such as attending the conferences, client visitation (Dolnicar, 2002; Fawzy, 2010) and representing a company which is often a multinational business (Fawzy, 2010; Holtbrügge et al., 2006). Therefore, IBTs are also defined as those individuals who travel solely for visiting their company’s clients, attending business meetings, exhibitions, or conferences and are willing to spend overnight outside of their usual environment for work-related activities (Cobanoglu et al., 2003).

IBTs normally experience a higher level of pressures and tensions as they not only represent their organizations but also participate in important meetings or conferences for activities such as knowledge transfer, negotiations, discussions, coordination, consultation and personal relations (Shaffer et al., 2012; Tam et al., 2008) so it is crucial for them to arrive to the destination on time and reduce any other potential costs (Tam et al., 2008). The amount of travel cost and time IBTs spend on their travel arrangements and accommodations has decreased in recent years; however, IBTs still are more sensitive to travel time compared to non-business travellers (Bulchand-Gidumal et al., 2011). According to Lin and Chen (2012), business travellers spend less time in the transport terminals and experience higher level of time pressure compared to leisure travellers. Assaf et al. (2013) measured firm performance using both desirable and bad outputs. They found that IBTs would be forced to invest more time in their business trips, for example, taking earlier flights due to the risk of delay. Therefore, it is likely that any delay could cause overall switching behaviour and negative word of mouth. Another factor, which differentiates IBTs from leisure travellers is the price. Most IBTs have strict needs about travel time and will seldom desire for lower prices. They are not price sensitive because they are restricted by time inflexibility (Chen and Schwartz, 2008; Harris and Uncles, 2007; Hung et al., 2010; Kulendran and Witt, 2003; Millar et al., 2012; Millar and Baloglu, 2011; Ting and Huang, 2012). They also look for comfort on business journey and more likely to believe that a higher price will result to a higher quality experience (Ye et al., 2012).

According to Ghiselli et al. (2015) IBTs have more intention to use search engines or even the brand website to learn more regarding the company-recommended hotels rather than their leisure counterpart. Cobanoglu et al. (2003) have conducted a study among Turkish male and female IBTs on the importance of hotel selection components and revealed that there are some differences with regards to information supplied in brochures or advertisements, also illustrated that females were more concerned about the security of the hotel, whereas male IBTs emphasized more on complimentary goods, food and beverage facilities, and parking services; furthermore, there are some aspects considered important by both genders such as technology, amenities and services. Likewise, female IBTs consider security, personal services, room services and low price as the most important factors for selecting a hotel (Chan and Wong, 2006; Memarzadeh et al., 2015; Tsai et al., 2011).

Literature review and hypotheses development

Traveller risk perception

The risk perception has been used broadly for more than four decades and has adapted by researchers concerned with destination image specifically after the 11th of September terrorist attack (Korstanje, 2009, 2011; Yang et al., 2015). Risk perception concerns how individuals evaluate the risk intrinsic in different positions (Dalborg et al., 2015). Risk can be defined as, what is experienced and perceived by the international travellers during the process of visiting a foreign country (Reisinger and Mavondo, 2006). Tourism is an industry interconnected upon global risk factors and political instabilities such as war in Egypt, Tunisia; health threats like influenza; crime, violence and terrorism activities which occurred after the September 11 attacks as well as natural disasters in Thailand and Japan (Bianchi, 2006; Seabra et al., 2012). Nevertheless, the importance and characteristics of these risks could vary from one particular destination to another, especially with the choice associated with international travel (Sharifpour et al., 2014). Travellers risk perception implies the path within which persons directly see and judge the degree of associated risk with a specific exposure or danger (Aschauer, 2010; Thomas et al., 2003).

A study conducted by Aro et al. (2009) on travellers risk perception among IBTs in Finland who visited South East Asian countries within the avian influenza epidemic in 2004 found that 6.3% of travellers who were on business journey had health risk-taking tendencies. According to Korstanje (2009) travellers risk perception is lower in IBTs in comparison with holiday travellers. In another study by Henthorne et al. (2013) in the context of cruise tourism in Jamaica found that travellers’ return intention has been significantly influenced by risk perception and also pointed out that, as the level of risk perception raises, the intention to return to a particular destination declines because risk can generate anxiety, fear and also insecurity. Rittichainuwat (2013) mentioned that if the IBTs had a pleasant travel experience with a particular destination it could effect on the level of their sensitivity towards risk and minimize their risk perception. Travellers risk perception is a preoccupation of modern society and has multidimensional construct consisting of a number of primary risk forms, for example physical, financial, performance, time and socio-psychological (Fuchs and Reichel, 2011; Sharifpour et al., 2013).

Physical risk

Physical risk refers to the risk that the tourism products can cause some problems with regard to safety (Fuchs and Reichel, 2011; Reisinger and Turner, 1999) or the likelihood of travellers encountering physical hazard, damage or sickness while on holiday (Chew and Jahari, 2014). An example of physical risk can be Cape Town in South Africa, where malaria was a concern. Thus, visitors are being advised to have a vaccination to prevent malaria; moreover, the researcher found that the dimensions of physical risks such as functional, physical, financial, socio-psychological and time are connected to the risk of being hurt or injured as a victim of violence (Björk and Kauppinen-Räisänen, 2012). Boksberger et al. (2007) in their study assessed the role of risk perception in air passengers between male and female behaviours and revealed that risk perception components obtained higher scores from female participants.

Financial risk

Financial could be defined as those travellers, who do not achieve value for money, waste or lose money and their travel expectations are not attained (Park and Reisinger, 2010). Financial risk in destinations could deter tourism expansion (Scott et al., 2012). Getz and Carlsen (2005) in their study stated that there are some challenges approaching tourism and hospitality businesses specifically in rural areas as most of them are self-financed and have difficulties in receiving financial loans which can expose the business to high financial risks and can quickly make them lack ability to grow or adapt.

Performance risk

Performance risk is the potential loss due to failure after purchasing an item (Laroche et al., 2004) or the risk recognized with the possibility that the goods will not function as anticipated or will fail (Fuchs and Reichel, 2011). According to Fuchs and Reichel (2006) in tourism and hospitality industry performance risk includes weather, crowded sightseeing, possible strikes, inappropriate tourist facilities, unfriendly locals, discourteous hospitality employees and low-quality food. In fact, consumers’ assessment of risk performance is according to their understanding and cognitive abilities of product domain. Performance risk could affect traveller word of mouth and switching intention to another destination (Sun, 2014).

Time risk

Time risk indicates the possibility that the travel experience may take longer time or the traveller will lose or waste time (Park and Reisinger, 2010). In a study by Jou et al. (2011) at airport ground access mode choice behaviour has revealed that travel time is the most important factor for IBTs. In order to avoid wasting travellers’ time, effective communication must be provided for facilitating easy access by travellers while they are seeking any information at the particular destination (Ortega and Rodriguez, 2007).

Socio-psychological risk

It refers to the fear that the good will not match an individual’s self-image (Fuchs and Reichel, 2011). The sociological and psychological aspects of consumption are significant due to the interplay between consumers and producers (Williams and Soutar, 2009). Likewise, the role of travellers’ preference is also related to socio-psychological needs (Qi et al., 2009). Dewett (2007) stated that the impact of intrinsic motivation on creativity is transferred through an increased willingness to take risks; therefore, intrinsic motivation was significantly linked with willingness to take risks. Kerr et al. (2004) in their study regarding motivation and the level of risk among male and female students’ recreational sport in Hong Kong and found that general life tendency differs across male and female motivation of obtaining activities at various degree of risk.

Destination image

The first studies in the 1970s on destinations’ image by Gunn (1972), Hunt (1975) and Mayo (1973) received great attentions from the researchers, and after nearly five decades destination image is still considered as a favoured area by many researchers (Deng and Li, 2013; Frías et al., 2012; Qi et al., 2009; Stepchenkova and Li, 2014; Yilmaz et al., 2009). Destination image has not only been focused by tourism researchers (Chen et al., 2013; Kaplanidou, 2009; Lai and Li, 2012; Li and Wang, 2011; Tasci and Gartner, 2007) but also become one of the most popular topics and pervasive areas, which are widely acknowledged in other disciplines such as marketing (Alvarez and Campo, 2014; Lee and Lockshin, 2012; Zhang et al., 2015). Destination image is commonly accepted as an important aspect in successful tourism development (Dolnicar and Grün, 2013; Tasci and Gartner, 2007) and play a key role in improving destinations’ attractiveness and competitiveness (Sancho Esper and Álvarez Rateike, 2010). Moreover, it plays a fundamental role in promoting tourist destinations (Ramkissoon et al., 2011).

Several researchers have defined destination image. Chen and Funk (2010) in their study mentioned that researchers can select a single or multiple definitions for destination image based on their specific study purposes. Liu (2014) defined destination image, as how people perceive an image of a destination and how the perception affects people’s behaviour in terms of destination selection. It is also defined as the interpretation of all objective knowledge, impressions, prejudices, imagination and feelings that a traveller holds of a particular destination (Moon et al., 2013; Shahijan et al., 2015; Wang and Hsu, 2010). Similarly, many researchers interpret destination image implies an individual’s mental representation of feelings, knowledge, ideas and the whole perception and expectations towards a particular destination (Assaker and Hallak, 2013; Jalilvand et al., 2012; Kim and Perdue, 2011; Lee et al., 2005; Truong and King, 2009).

Many researchers consider destination image to be important as it plays a significant role in attracting travellers to select a specific destination (Byon and Zhang, 2010; Kim and Perdue, 2011; Kim et al., 2013b; Moon et al., 2013; Song et al., 2014; Tavitiyaman and Qu, 2013; Zhang et al., 2014). It is also important because of its influences on travellers’ level of satisfaction (Liu, 2013). Destination image also leads to realistic expectations and in turn satisfying visitors (Lakshmi and Ganesan, 2010). Prior to visiting a destination, destination image is based on visual rather than the actual image (Tasci and Gartner, 2007). Therefore, visual images are strong factors of marketing of the destination and dominate all types of tourism promotion from brochures as well as television commercials to online websites (Milman, 2012). Destination image can focus narrowly on what interests visitors of a country such as the standards of its hotels and its places of interests (Lee and Lockshin, 2011). Moreover, researchers emphasized that the idea of destination image is not static but can change over time (Chen et al., 2014; Lepp et al., 2011). If potential travellers have lack of knowledge regarding the destination, image could perform as a significant function and positive, strong and favourable images could raise the probability of selecting a destination by travellers (Hyun and O’Keefe, 2012; Lee and Lockshin, 2011; Tavitiyaman and Qu, 2013). Destination images were well known/popular and raise awareness due to its significance, and it is important to realize that travellers’ perceive destination image variously when they want to choose a particular destination (Veasna et al., 2013).

Destination image can be developed based on the estimation or understanding of a region’s characteristics (Moon et al., 2011). Many researchers studied and discussed about different aspects of destination image, including its cognitive, which pertains to the knowledge as well as belief regarding the physical characteristics of a particular destination. Moreover, in comparison with affective image, cognitive component of destination image is more measurable, observable and also descriptive (Bigné Alcañiz et al., 2009; Chen and Phou, 2013; Stylos et al., 2016; Xie and Lee, 2013) affective which refers to the evaluation of the affective quality of feeling regarding characteristics and the surrounding environment. Furthermore, there is no uncertainty regarding the worth of affective image in order to understand traveller’s behaviour (Chew and Jahari, 2014; Hosany et al., 2006; Nicoletta and Servidio, 2012; Royo-Vela, 2009; Stylos et al., 2016; Wang and Hsu, 2010; Xie and Lee, 2013); conative aspect is considered analogous to behaviour and derived from cognitive and affective images. Similarly, the conative aspect of destination image represents traveller’s consideration of a place as a potential travel destination (Garay Tamajón and Cànoves Valiente, 2015; King et al., 2012; Mwaura et al., 2013; Stylos et al., 2016; Wong et al., 2015; Xie and Lee, 2013), overall image, cognitive affective joint image, self-congruity (Zhang et al., 2014), behavioural dimensions, relativistic and dynamic nature, formation (Beerli and Martín, 2004b; Frías et al., 2008; Kim et al., 2012) and agents, measurement (Royo-Vela, 2009). Similarly, another study by Noort et al. (2008) stated that financial risk perception is impacted by image of the consumers in the online store.

Liljander et al. (2009) in their study in Finland investigated the attitudes of buying a retailer-endorsed brand or apparel retailing brands among consumers and stated that the social risk has been reduced and the quality of brands has been raised due to store image. Moreover, another study by Qi et al. (2009) in China Olympic Games’ claimed that, risk perceptions as well as safety are two significant factors in order to form an overall image of a particular destination, specifically when a country hosting the Olympic Games which is engaging with image construction. However, risk perception is interrelated with destination image. Likewise, the empirical research by Noh and Vogt (2013) tested a tourism behaviour model explaining travellers’ intentions to travel to East Asian countries and the model showed the mutual indicative influence of cognitive destination image and perceived risks associated with vacationing at that destination on affective Destination Image (DI). Therefore, it was shown that perceived risk correlated negatively to affective image. Thus, we hypothesize that:

H1 : Destination Image has a positive effect on traveller risk perception

Traveller experience

The term experience originated in the 1960s and represents a complex construct, which is postulated distinctively from everyday life experiences in the field of tourism (Neuhofer and Buhalis, 2012). Traveller experience has been an established area of research during the last three decades (Rageh et al., 2013). However, traveller experience is a focal point in tourism research and all travel experiences are subjective and unique to each individual (Mathisen, 2012; Pappalepore et al., 2014). Kang and Gretzel (2012a) defined traveller experience as a permanent flow of sensation and thoughts within moment of consciousness. It follows a history of past experiences as well as expectations and has connected with mental concept closely. Experiences, whether ordinary or extraordinary, transform lives, acting as a means to construct reality; therefore, the facilitation of extraordinary experiences has become a desired goal in the tourism industry (Agapito et al., 2013).

Experience can be divided into direct and indirect experiences based on different types of situations. Direct experience is significant because it reflects the traveller experience of the real environment (Wang et al., 2012). Indirect experience lead travellers to experience negatively, which deducts the consumption and purchasing behaviour frequency as well as having a negative impact on people’s purchasing behaviour (Wang et al., 2012). Lauring et al. (2014) reported that, travellers who have experience are more inclined to go on a trip in order to fulfil higher degree of needs and desire. In addition, travellers’ positive experience with visited destination has different advantages such as increasing satisfaction and encouraging their positive behaviour and attitude towards the visited destination (Io, 2013). Likewise, positive traveller experiences lie at the heart of successful tourism (Pearce et al., 2013). Therefore, traveller experience could be formed by different personalized factors, such as past experience, motive, individual personalities or an individual with whom a place or activity is experienced (Komppula and Gartner, 2013). For instance, an IBT who is visiting a particular country is mainly impacted by former experience with the hotel itself, hotel employees who provided good service and recommendations by companies (Chan and Wong, 2006; Tsai et al., 2011). Three facets of traveller experience such as escape, enjoyment and learning are also frequently recognized across tourism literature (Kang and Gretzel, 2012a). Similarly, Kang and Gretzel (2012b) specified learning and enjoyment as significant characteristics of enhanced tourism experiences.

Learning experience

It refers to an experience generated by travellers regarding the information and knowledge about new things (Kang and Gretzel, 2012a). According to Mitchell (1998), learning experience is recognized as crucial even though it is not an essential key stimulating element for travelling. Similarly, study has done regarding the cultural tourism in Turkey, found that, learning has the significant impact on the expanding of experience quality (Cevdet Altunel and Erkut, 2015).

Enjoyment experience

It refers to the degree of traveller experience perceived to be delightful/enjoyable part from the utilitarian value of the experience (Kang and Gretzel, 2012a). According to Wang et al. (2012), enjoyable experience evolved from the sensations generated when consumers buy goods. Moreover, enjoyment can exist via experience of being immersed and the outcomes of that immersion (Dong and Siu, 2013). Enjoyment experience can significantly affect on spreading positive word of mouth and destination revisit intentions (Hart et al., 2007). According to Seebaluck et al. (2013) the enjoyment of memorable and noteworthy experience is extremely significant.

Escape experience

It is defined as an experience by which travellers feel immersed in the surroundings at destination and separated from the constraints of common life (Kang and Gretzel, 2012a). Tourism is a path for the individual to escape from the monotony of daily life and get back to the routine life after experiencing non-routine life. Hence the experience of escapist has been investigated in tourism research repeatedly (Oh et al., 2007). Moreover, adventure or escape from fatigue is likely to compel positive emotions, thereby improving satisfaction (Duman and Mattila, 2005). Barnett and Breakwell (2001) examined the relationships between individuals’ experience and risk perception among higher education students in UK and found that experience has a greater effect in relation to more concerned risks. Several researchers (Chen and Gursoy, 2001; Creyer et al., 2003; George, 2010; Kozak et al., 2007; Lepp and Gibson, 2003; Rittichainuwat and Chakraborty, 2009) suggested that while respondents’ contact and experience with a destination increase, their risk perception degree declines and their attitudes towards international tourism will improve. Finally, Creyer et al. (2003) stated that as experience is obtained, an individual will illustrate a greater orientation for risk. Experience is anticipated to have a direct and significant impact on risk orientation.

H2 : The higher traveller experience reduces risk perception

Different number of studies supports the association of traveller experience and destination image. For instance, Bigné Alcañiz et al. (2009) in their study analysed the image cognitive component of a destination among travellers in Spain and found that destinations compete principally through their image. Moreover, the prime image can be changed during the holiday experience, and also the destination loyalty will be strengthened when tourist image continues to be positive and favourable. Furthermore, the traveller’s Internet experience also applied a moderate impact on the destinations prime image. Rodríguez Molina et al. (2013) have conducted a study regarding past experience moderating role in the destination’s image formation in Spain and found that experience has a moderating impact on the cognitive image formation. Furthermore, a study conducted by Andreu et al. (2001) on destination image in Spain illustrated that, whenever there is high level of dissatisfaction with the particular destination, individuals will decide to travel to alternative destinations in future.

Previous studies in Asian countries (Chew and Jahari, 2014; Gibson et al., 2008; Lee et al., 2008) also support the positive relationship between travellers’ experience and destination image. For example, Chew and Jahari (2014) conducted a study on destination image as a mediator between perceived risks and revisit intention among Malaysian travellers who visited Japan and found that repeated travellers are likely to refer to past travel experience in a country to form overall destination image of that country; moreover, it is also stated that evaluation of the probability of positive destination experience may influence destination image. Another study by Lee et al. (2008) differentiated the distinction of the night market experience and image between residents and international travellers in Taiwan and found that residents who live temporary have stronger thinking experiences, sense, feel, as well as stronger images of local food, products, price, bargain, atmosphere and reputation and it is suggested that experience is highly correlated with image. In addition, a study by Gibson et al. (2008) regarding destination image and visit intention to China and the Beijing 2008 Olympic games revealed that destination image is significantly affected by foreign travel and Asian travel experiences. Furthermore, it is also expressed that, when travellers visit the chosen destination, they will create more complex image which results from actual contact and experiences in the area.

Experience can renew destination image and serves as a new input in continued formation of attitude (Chen and Funk, 2010). Moreover, Pan et al. (2014) explored the correlation between image dimensions, motivation and it was revealed that it is equally important to realize the travellers experience and their achieved travellers destination image via analysing their post-visit narratives. Positive image and positive travel experience will result in a moderately positive evaluation of a destination, whereas a negative image and a positive experience will reflect in an extremely positive evaluation of a specific destination (Jenkins, 1999; O’Leary and Deegan, 2005). Thus, we hypothesize that:

H3: Traveller Experience has a positive effect on Destination Image

ISs

The ISs for travellers have changed greatly over the past 15 years due to emergence of new technologies, changes in consumers’ behaviour and increase in the number of tourist destinations (Molina et al., 2010). ISs are known as motivation elements or generating image, which can impact the formation of assessment and perception (Beerli and Martín, 2004a; Gil and Ritchie, 2009). IS has been changes due to new technologies for the tourism activities over the 10–20 years (Molina and Esteban, 2006). Currently, accessibility to the information is easy and more information available to the public via a broad range of channels (Kim and Sin, 2011). Access to the information is crucial especially when travellers have limited information regarding a particular destination and never visited that place before (Milman, 2012).

Rodríguez-Santos et al. (2013) have divided ISs into primary and secondary information. Primary information is drawn from the visit to the place therefore modifies the image that traveller had before getting to know the destination. Secondary ISs are those consulted before visiting the location such as travel agencies, friends, colleagues or the websites. Other researchers classified ISs into internal and external sources. Internal ISs include personal experience and knowledge accumulated through an ongoing information search (Björk and Kauppinen-Räisänen, 2015; Li and Wang, 2011).

People search for travel information for the purpose of decreasing the level of uncertainty when choosing a destination (Siu-Ian and Alastair, 2003). Woudstra and Hooff (2008) in their study indicated that the travellers are paying a lot of attention to the quality of the source when planning which ISs to use. Traditionally, ISs were mainly allocated to the pre-visit, for instance television advertisement, newspaper and magazine articles as well as sources like highway information, road signs and maps usually used during a journey (Kah et al., 2011). Chen (2000) has conducted a comprehensive study between Japanese, South Korean and Australian IBTs to the United States and found that IBTs were inclined to use airlines, corporate travel sections, travel agencies, and television or radio as the source of information. The type of information travellers’ use is related to age, income, education and other related demographic factors and what is important to one particular traveller may not be important to another traveller (Rompf et al., 2005).

Different types of IS have been studied in tourism researches such as Internet, travel agencies and friends and relatives, news media and commercial brochures (e.g. Bieger and Laesser, 2000; Dey and Sarma, 2010; Kim and Prideaux, 2005; Lakshmi and Ganesan, 2010; Molina et al., 2010; Özel and Kozak, 2012; Patterson, 2007; Phau et al., 2010; Yilmaz et al., 2009) The availability of tourist ISs has been extended by use of the Internet, which contains several features, for example: travellers use Internet to watch television, radio or read magazines, newspapers as well as sending e-mail. It is also different from other types of sources in terms of accessibility, bilateral communication meaning the communication between the destination and the travellers, real time information as well as convenience in updating (Castañeda et al., 2009; Luo et al., 2005). In the travel and tourism context, the Internet has become a significant medium and increasingly is recognized as an additional important IS for travellers (Chiang et al., 2014; Luo et al., 2005; Zehrer et al., 2011). The Internet also plays a major role as IS for travellers’ decision-making process and it continues to become an essential part of everyday life (Zhang et al., 2011). Accordingly, 65% of business travellers use Internet for the purpose of obtaining information by referring to ‘TripAdvisor’ website as the largest travel community in the world that provides a broad range of information to travellers (Bulchand-Gidumal et al., 2011; Hsu, 2012). Moreover, rapid increase of Internet users has motivated tourism organizations to implement technologies such as Internet as a part of their marketing and communication strategies (Ruiz-Molina et al., 2013). Huang and Zhou (2013) asserted that most Internet users are used to obtain travel information from Internet, then establish contracts with travel agencies, negotiate prices and face-to-face contact. As a result, travel agencies will carry on to play a significant role as IS in tourism industry (Seabra et al., 2007).

IS variety and types have an influence on the travellers’ image of the destination, particularly in the initial image formation during the pre-visit stage (Tang-Taye and Standing, 2013). Recent researches claimed that destination image is influenced by travellers’ prior knowledge, experiences, commercial and non-commercial ISs because people regard non-commercial ISs as having higher credibility (Lin et al., 2012; Stepchenkova and Li, 2014). Nowadays Internet users receive both symbolic and social stimuli on blogs and micro blogs (Tse and Zhang, 2013; Um and Crompton, 1990). Campo and Alvarez (2013) in their study found a significant influence of promotional material on the country image and tourism destination image of Israel. They further mentioned that, for example, tourism brochures may enhance the country image and the destination, even when the preliminary perception is negative. In a similar study, Phau et al. (2010) on perceived destination image and destination choice intention of university student travellers to Mauritius found that ISs have an influence on perceived destination image and destination choice intention. Another study by Aloudat and Rawashdeh (2013) regarding Jordan destination image found two types of destination image as organic (mental) and induced (initial) images and discussed that initial destination image is shaped from the information achieved via commercial sources such as advertising companies and travel agencies, and also demonstrated that formation of destination image is influenced by IS.

Conversely, Frías et al. (2008) on pre-visit formation of destination image in Spain via choosing two types of IS: Internet and travel agencies found that destination image can be impacted negatively by the Internet. The results indicated that customers who use travel agencies understand a better destination image compared to those who prefer to use Internet for gathering required information on destination. Another study by Molina et al. (2010) has identified image features such as quality of accommodation, sports facilities and different customs/culture that contribute to build a positive destination in Spain and they supported the relationship between these variables. The results of their study show that ISs have a strong influence on tourist destination image. In conclusion, different researchers confirmed the significant impact of IS on destination image (Frías et al., 2012; Jalilvand et al., 2012; Lim et al., 2013; Ryu et al., 2013). Thus, in this study we hypothesize that:

H4: Information Source has a positive effect on Destination Image

Sharifpour et al. (2013) investigated the role of prior knowledge among Australian tourists’ decision-making process and recommended that while different dimensions of risk perception may evoke the use of IS, prior knowledge also plays a significant role besides, risk perception in specifying the IS used. They further on the effective communication of risk claimed that receptiveness to information on risk is affected by IS. According to Reisinger and Turner (1999) financial risk should refer to the personal ISs such as friends, colleagues and relatives. On the other hand, if they are concerned about the time risk it is better to consult with the travel agents and other non-personal ISs (Gursoy and McCleary, 2004). It is posited that high credibility of ISs like trust in risk management can be inversely correlated with traveller risk perception. Thus, we hypothesize that:

H5 : Information sources has a positive effect on Traveller Risk Perception

Motivation in repurchases increases when consumers gained useful information from their previous purchase experience. Therefore, previous experience stored in travellers’ memories can be viewed as a credible IS (Manthiou et al., 2014). Travellers’ decision-making process is associated with their incentives and also related to confirmation bias in consumer behaviour so motivated travellers might search for information which is close to their needs via relatives, friends and colleagues, guide books, TV channels or most importantly Internet (Aziz and Ariffin, 2009). Kim et al. (1996) in their study found a significant relationship between motivation and ISs in the senior travel market. Kim et al. (2013a) investigated the motivation of undergraduate American students for using social networking sites in travel information and found that users’ motivations are more powerful when they interact with other users. Equally, another study by Elenbaas et al. (2013) on the impact of media coverage and motivation on performance-relevant information had shown that the nature of the interaction between information availability and motivation depends on the actual level of information saturation in the media environment. Beaudoin (2008) examined the relation between Internet use and interpersonal trust among American adults and found that there is a significant path from social resource incentive for Internet use to perceived information. Thus, we hypothesize that:

H6 : Information Sources has a positive effect on Traveller Motivation

Traveller motivation (TM)

Motivation has initially developed in the 1930s and can be illustrated as consisting of inner elements that arouse as well as infuse an individual behaviour (Kirkup and Sutherland, 2015). The term motivation comes from the Latin movere (to move) (Tran and Ralston, 2006). Motivation implies biological and psychological needs comprising the integral forces that stimulate, guide and integrate an individual’s behaviour and activity (Aziz and Ariffin, 2009; Van der Merwe et al., 2011). In other words, those necessities that make a person or an individual direct their actions in order to satisfy a need (Sancho Esper and Álvarez Rateike, 2010). Motivation and motivational factors have been at the centre attention among tourism scholars and can be considered as one of the most interesting research areas in the tourism literature (Farmaki, 2012, 2013; Huang, 2010; Kim and Eves, 2012; Lee et al., 2011; Pearce and Lee, 2005). Motivation of travellers begins when an individual becomes attentive of some needs and realizes that particular destination might have the capability to fulfil those needs (Beh and Bruyere, 2007). Thus, it is known that TM is a significant construct in tourism which can contribute to the conduct and behaviour of travellers (Jeong, 2014; Peter and Anandkumar, 2015). Rittichainuwat et al. (2014) in their study regarding TMs during financial crisis revealed that although discounts are usually a major travel motivation, they do not motivate travellers to travel during financial crises.

People’s motivation changes with their traveller experience (Pearce and Lee, 2005) and could be modified according to the amassed travel experiences (Wong and Musa, 2014). TM consists of five various stages, namely safety/security needs, relationship needs, self-confidence and expansion needs, self-actualization and fulfilment needs, relaxation (Park and Yoon, 2009; Pearce and Kang, 2009) as well as socialization (Prebensen et al., 2013). Xu and Chan (2010) reported that the notions of travellers’ motivation and experience are significantly associated with each other. Similarly, Chang et al. (2010) stated that the correlation among motivation and experience are significant notions in order to understand the tourist study. Lee (2005) indicated that the relationship between procrastination and motivation was caused mainly by the covariance between flow experience and motivation. Prebensen et al. (2013) found that motivated travellers will involve more in their trip experience in comparison to travellers with no choice over their destinations such as IBT. Therefore, we hypothesize that:

H7 : Traveller motivation has a positive effect on traveller experience

Research methodology

In order to conduct the statistical analysis and test the proposed hypotheses, a quantitative method was selected using cross sectional data collection. As the purpose of this study, IBTs were target respondents to assess the casual relationship between travellers risk perception, motivation, IS, traveller experience and destination image. A questionnaire was designed which consisted of two sections to capture information on experienced business travellers of Iran. In the first section of survey, respondents were asked demographic information such as age, gender, nationality and educational background. In addition, respondents were also asked (1) purpose of travelling and (2) frequency of their visit to Iran. These two questions were embedded into the survey to make sure that the respondents are experienced and their purpose is business. To measure destination image, six items were adopted from previous studies (Assaker et al., 2011; Jalilvand and Samiei, 2012a; Veasna et al., 2012). As a first factor construct, three items for learning experience, three items for enjoyment experience and three items were adopted to measure escape experience (Kang and Gretzel, 2012a). Five items were adopted to measure ISs (Frías et al., 2008) and seven items to measure motivation (Xie et al., 2008). Lastly, to measure risk perception as a second-order construct, five items to measure financial risk, seven items for physical risk, eight items for performance risk, five items for socio-psychological risk and three items to measure time risk adopted from Fuchs and Reichel (2006). Appendix 1 shows the measurement items.

Prior to data collection, a pre-test (pilot study) was carried on to assess the internal consistency and ensure that questionnaire is free of wording errors. Therefore, 24 questionnaires were collected from the target population and were further discussed with tourism researchers and tourism mangers. All reflective constructs measurements had high levels of internal consistency reliability (Cronbach’s Alpha). After few amendments, the questionnaire was finalized. A total of 315 questionnaires were distributed among IBTs from which 242 questionnaires were obtained. Out of 242 questionnaires eight of them were not filled completely, thus, the total valid questionnaires were 234 (response rate: 76.82%). Table 1 shows the demographic profile of respondents. As shown, out of 234 respondents, 14.5% of respondent were travelling for business meeting, 34.3% for negotiations, 32.5% for marketing and 18.6% others for business motivation. Furthermore, 35.51% of respondents were visiting Iran for first time, 40.7% for second time, 14.0% for third time and 9.9% above three times. Table 1 presents the demographic profile of respondents.

Table 1.

Demographic profile of respondents.

Profile	Category	Percentage (%)
Age	18–24	5.2
	24–31	26.2
	31–37	48.8
	Above 37	19.8
Gender	Male	57.0
	Female	43.0
Nationality	China	7.6
	Pakistan	14.5
	Turkey	9.3
	Oman	15.1
	UAE	17.4
	Malaysia	19.2
	Indonesia	1.2
	India	15.7
Education	Diploma	5.8
	BS	22.7
	Master	48.3
	PhD	17.4
	Others	5.8
Purpose of	Business meeting	14.5
travelling	Negotiation	34.3
	Marketing activities	32.5
	Other business purposes	18.6
Frequency of	One time	35.5
travelling to Iran	Two times	40.7
	Three times	14.0
	Above three times	9.9

Dealing with missing values

The primary data were collected through a questionnaire, which had missing values problem. This issue occurred because some of the questions are not answered or missed by respondents and creating an issue in social science research (Schafer and Olsen, 1998). Therefore, expectation maximization algorithm (Little, 1988) was performed in order to impute missing values using SPSS software (Version 19). Little’s missing completely at random χ2 statistics obtained from this procedure and we found that missing data are at random. Thus, we run expectation maximization procedure (Graham et al., 1997) to impute missing values and handle missing values problem.

Common method variance (CMV)

CMV also might appear due to single survey method in data collection (MacKenzie and Podsakoff, 2012; Podsakoff et al., 2003; Zheng et al., 2012) which is attributable to the measurement method rather than to the constructs the measures represent (Podsakoff et al., 2003; Rezaei, 2015). We address this issue (CMV) using the guideline proposed by Podsakoff et al. (2003). First, at the questionnaire design stage the common scale anchors were avoided using six anchors and seven anchors for endogenous construct and exogenous constructs and several other steps. Second, after the data were collected, statistical techniques (i.e. Harman’s one-factor test) were conducted. Therefore, our statistical results demonstrate that CMV is not a concern in this study and we proceed with further analysis.

Non-response bias

Failing in report and assess the non-response bias affect the generalizability of findings (Michie and Marteau, 1999). Lewis et al. (2013) defined non-response bias ‘as a systematic and significant difference between those who respond to a survey and those who do not in terms of characteristics central to the research focus’ (240–241). Based on the continuum of resistance theory (Lin and Schaeffer, 1995) we ensure that non-response bias is not an issue in this study. Accordingly, three steps were taken to ensure that the non-respondent is not an issue in this study. First, analysis of known demographic profiles; second, wave analysis; and finally comparing key construct of study such as risk, destination image and experience shows no significance differences between groups using t-test analysis.

Data analysis method

Structural equation modelling (SEM) was employed using partial least squares (PLS) analysis to assess measurement and structural model for reflective constructs and test the proposed research arguments. SEM is a ‘quasi-standard’ in marketing research which it enables researchers to test complete theories and concepts (inner or structural model and outer or measurement model) (Hair et al., 2011). In addition, PLS path modelling becomes an essential method in empirical research (Rezaei, 2015; Sarstedt et al., 2011). PLS-SEM is a ‘causal modelling approach aimed at maximizing the explained variance of the endogenous latent constructs’ (Hair et al., 2011: 139). PLS-SEM is preferred over covariance-based SEM (CB-SEM) as the model is complex, and if we assume that measured variance is useful for explanation/prediction of structural relationships (Hair et al., 2013). PLS-SEM is, however, advantageous compared to CB-SEM when analysing predictive research models that are in the stages of theory development (Gimbert et al., 2010; Rezaei, 2015) and the statistical objective in PLS-SEM is to maximize the explained variance of the endogenous latent constructs (Hair et al., 2013). Therefore, PLS-SEM was found suitable in this study to conduct and assess measurement and structural model for reflective constructs and test the proposed research arguments using SmartPLS software developed by Ringle et al. (2005).

Findings

Measurement model assessment

Followed by Hair et al. (2013), to evaluate reflective measurement model, we examine outer loadings (item loading), composite reliability (CR), average variance extracted (AVE = convergent validity) and discriminant validity. As shown in Table 2, all outer loadings are well above 0.70. The reflective measurement specifications are shown in Figure 1 (item loadings). All reflective constructs measurements also have high levels of internal consistency reliability which is indicated by the above CR values and Cronbach Alpha. Furthermore, shown in Table 2, AVE values (i.e. convergent validity) are well above the minimum required level of 0.50, demonstrating validity for all research constructs.

Figure 1.

Result of PLS algorithm (item loading, path coefficient and of R²).

Table 2.

Construct validity for reflective scales.

Research construct	Item	Item loading	AVE^a	CR^b	Cronbach Alpha
Destination image	DI1	0.775	0.684	0.928	0.907
	DI2	0.843
	DI3	0.851
	DI4	0.855
	DI5	0.829
	DI6	0.806
Escape experience	EE1	0.884	0.773	0.911	0.853
	EE2	0.879
	EE3	0.875
Enjoyment experience	EJE1	0.895	0.788	0.917	0.865
	EJE2	0.876
	EJE3	0.891
Learning experience	EL1	0.899	0.814	0.929	0.886
	EL2	0.907
	EL3	0.900
Information sources	IS1	0.873	0.758	0.940	0.920
	IS2	0.863
	IS3	0.898
	IS4	0.858
	IS5	0.860
Motivation	MT1	0.828	0.689	0.939	0.925
	MT2	0.834
	MT3	0.852
	MT4	0.828
	MT5	0.845
	MT6	0.818
	MT7	0.804
Financial risk	RPF1	0.835	0.699	0.921	0.892
	RPF2	0.828
	RPF3	0.849
	RPF4	0.844
	RPF5	0.823
Physical risk	RPP1	0.838	0.732	0.956	0.947
	RPP2	0.879
	RPP3	0.865
	RPP4	0.894
	RPP5	0.865
	RPP6	0.856
	RPP7	0.832
	RPP8	0.812
Performance risk	RPPR1	0.755	0.671	0.942	0.929
	RPPR2	0.800
	RPPR3	0.836
	RPPR4	0.852
	RPPR5	0.853
	RPPR6	0.842
	RPPR7	0.816
	RPPR8	0.793
Socio- psychological risk	RPS1	0.862	0.756	0.939	0.919
	RPS2	0.862
	RPS3	0.888
	RPS4	0.869
	RPS5	0.867
Time risk	RPT1	0.920	0.818	0.931	0.888
	RPT2	0.898
	RPT3	0.895

Average variance extracted (AVE) = (summation of the square of the factor loadings)/{(summation of the square of the factor loadings) + (summation of the error variances)}.

Composite reliability (CR) = (square of the summation of the factor loadings)/{(square of the summation of the factor loadings) + (square of the summation of the error variances)}.

To assess discriminant validity we used criteria from Fornell and Larcker (1981) and heterotrait–monotrait ratio. The off-diagonal values in the matrix (Table 3(a)) are the correlations between the latent constructs, and the shared values between the constructs are square of these correlations. The threshold value for heterotrait–monotrait ratio is below 0.90 (Table 3(b)) (Gold and Arvind Malhotra, 2001; Teo et al., 2008). Table 3(a) and (b) shows that discriminant validity between research constructs is met. In addition, according to loading and cross loading in assessment of discriminant validity, comparing the loadings across the columns and indicator’s loadings on its own construct are in all cases higher than all of its cross loadings with other constructs. Therefore, results implying that there is discriminant validity between all the constructs.

Table 3a.

Discriminant validity – Fornell Larcker criterion.

Research construct	Destination image	Enjoyment experience	Escape experience	Financial risk	Information sources	Learning experience	Motivation	Performance risk	Physical risk	Socio- psychological risk	Time risk
Destination image	0.827
Enjoyment experience	0.615	0.887
Escape experience	0.665	0.673	0.879
Financial risk	0.656	0.609	0.617	0.836
Information sources	0.612	0.586	0.623	0.541	0.871
Learning experience	0.675	0.797	0.602	0.623	0.620	0.902
Motivation	0.642	0.644	0.618	0.575	0.659	0.673	0.830
Performance risk	0.682	0.638	0.628	0.662	0.589	0.677	0.603	0.819
Physical risk	0.588	0.546	0.528	0.578	0.469	0.527	0.515	0.619	0.855
Socio- psychological risk	0.678	0.679	0.650	0.690	0.576	0.690	0.588	0.632	0.601	0.870
Time risk	0.631	0.623	0.562	0.627	0.584	0.637	0.551	0.619	0.574	0.609	0.904

Note: The diagonals represent the square root of AVE and the off-diagonals represent the correlation.

Table 3b.

Discriminant validity – heterotrait–monotrait ratio.

Constructs	Destination image	Enjoyment experience	Escape experience	Financial risk	Information sources	Learning experience	Motivation	Performance risk	Physical risk	Socio- psycho- logical risk
Enjoyment experience	0.805
Escape experience	0.870	0.800
Financial risk	0.728	0.691	0.705
Information sources	0.887	0.656	0.704	0.596
Learning experience	0.752	0.810	0.805	0.700	0.686
Motivation	0.802	0.720	0.690	0.629	0.810	0.744
Performance risk	0.741	0.711	0.705	0.836	0.637	0.746	0.647
Physical risk	0.633	0.603	0.587	0.844	0.500	0.574	0.546	0.657
Socio- psychological risk	0.741	0.762	0.734	0.760	0.626	0.764	0.637	0.800	0.642
Time risk	0.701	0.710	0.644	0.702	0.645	0.831	0.605	0.791	0.623	0.894

Note: The criterion for heterotrait–monotrait ratio is below 0.90 (Gold and Arvind Malhotra, 2001; Teo et al., 2008).

Risk perception and experience as the two main constructs in this study considered as type I model (reflective–reflective) or hierarchical common factor model indicate that ‘lower-order constructs are reflectively measured constructs themselves that can be distinguished from each other but are correlated’ (Becker et al., 2012: 363). To empirically test the first-order construct on designated second-order construct, hierarchical component model (Chin et al., 2003) or repeated indicators approach (Lohmoller, 1988) as a popular approach in estimating higher order constructs with PLS (Wilson and Henseler, 2007) was undertaken. As presented in Table 4, t-statistics of first-order construct for all dimensions are statistically significant on the designated second-order construct. Further, all dimensions of the second-order constructs are significant at the 1% level. These paths do not signify hypotheses relationship but rather imply the contribution of each first-order construct to the designated second-order construct (Pallud and Straub, 2014). In addition, AVE and CR values for second-order construct in Table 4 show reliable values. As shown in Table 4, all items significantly contribute, statistically, to their prospect dimension/constructs. Therefore, our results support that experience as a second-order reflective construct comprising of escape experience, enjoyment experience, learning experience and risk perception comprising of financial risk, physical risk, performance risk, socio-psychological risk and time risk as a second-order reflective construct.

Table 4.

Weights of first order on designated second-order constructs.

Second-order construct	First-order construct	Item	CR	AVE	Path coefficient	T-statistic of first-order construct	T-statistic of item
Experience	Escape experience	EE1	0.940	0.839	0.897	48.175*	46.673*
		EE2					39.527*
		EE3					43.168*
	Enjoyment experience	EJE1			0.937	94.380*	53.058*
		EJE2					36.959*
		EJE3					49.054*
	Learning experience	EL1			0.913	65.441*	52.040*
		EL2					54.685*
		EL3					50.496*
Risk perception	Financial risk	RPF1	0.941	0.758	0.889	39.643*	22.098*
		RPF2					28.743*
		RPF3					30.189*
		RPF4					32.571*
		RPF5					26.704*
	Physical risk	RPP1			0.842	19.338*	21.126*
		RPP2					32.307*
		RPP3					38.308*
		RPP4					41.750*
		RPP5					38.120*
		RPP6					42.027*
		RPP7					32.773*
		RPP8					25.400*
	Performance risk	RPPR1			0.911	55.454*	16.555*
		RPPR2					27.352*
		RPPR3					35.647*
		RPPR4					37.921*
		RPPR5					30.302*
		RPPR6					30.172*
		RPPR7					25.480*
		RPPR8					21.302*
	Socio-psychological risk	RPS1			0.889	43.922*	38.373*
		RPS2					37.666*
		RPS3					38.833*
		RPS4					37.908*
		RPS5					39.581*
	Time risk	RPT1			0.818	23.034*	64.808*
		RPT2					49.678*
		RPT3					45.939*

Note*: t-value 2.58 (sig. level = 1%).

Structural model assessment

After measurement models were assessed and determined as valid and reliable, the next step was the assessment of structural model (Hair et al., 2013; Rezaei and Ghodsi, 2014) that would examine the model’s predictive capabilities and the relationships between proposed constructs. Before evaluating structural model, we assess the structural model for collinearity because the estimation of path coefficients in the structural model is based on regressions of each endogenous latent variable on its corresponding predecessor constructs. We found that there is no significant level of collinearity among the endogenous constructs as tolerance levels were well below 0.20 (VIF = 5.00). Furthermore, we evaluate the structural model in PLS-SEM using the significance of the path coefficients, the level of the R² values, the f² effect size and the predictive relevance (Q², q²). Accordingly, we performed PLS algorithm procedures to obtain estimates for the structural model relationships (the path coefficients) and test the hypothesized relationships between the constructs (see Figure 1). In addition to PLS algorithm procedures, we examined the significance of relationships by performing bootstrapping algorithm (using 5000 resample). Table 5 shows hypothesis testing, path coefficient, standard error and t-statistic for each hypothesis.

Table 5.

Hypothesis testing.

Hypothesis	Path	Path coefficient	Standard error	T-statistic	Decision
H1	Destination image → Risk perception	0.329	0.116	2.833*	Supported
H2	Experience → Risk perception	0.493	0.078	6.350**	Supported
H3	Experience → Destination image	0.436	0.060	7.247**	Supported
H4	Information sources → Destination image	0.521	0.057	9.132**	Supported
H5	Information sources → Risk perception	0.030	0.092	0.323	Not supported
H6	Information sources → Motivation	0.759	0.040	18.852**	Supported
H7	Motivation → Experience	0.705	0.047	14.953**	Supported

Note*: Critical t-values: two-tailed test 2.58 (significance level = 1%).

The results imply that all paths are statistically significant with high t-values, two-tailed test 2.58 (significance level = 1%) except the path between ISs and travellers risk perception (H5). Hypothesis 1, which proposes the positive relationship between destination image and risk perception, was supported with path coefficient of 0.329, standard error of 0.116 and t-statistic of 2.833. Hypothesis 2 (experience → risk perception) with path coefficient = 0.493, standard error = 0.078, t-statistic 6.350; and Hypothesis 3 (experience → destination image) with path coefficient 0.436, standard error = 0.060, t statistic = 7.247 was supported. Hypothesis 4 which proposes the positive relationship between ISs and destination image (path coefficient = 0.521, standard error = 0.057 and t-statistic 9.132) was supported while Hypothesis 5 (information sources → risk perception) with path coefficient = 0.030, standard error = 0.092 and t-statistic 0.323 was not supported and H6 (information sources -> motivation) with path coefficient 0.759, standard error = 0.040 and t-statistics = 18.852; H7 (motivation -> experience) with path coefficient = 0.705, standard error = 0.047 and t-statistics = 14.953 were supported. Furthermore, the R² values of the endogenous latent variables were also obtained from PLS algorithm as shown in Figure 1. The R² values for endogenous constructs were found high and medium predictive accuracy degree as destination image = 0.765, risk perception = 0.642, motivation = 0.576 and experience = 0.497.

Furthermore, after assessment of magnitude of the R² values as a criterion of predictive accuracy, we examine the Q² value that shows the model’s predictive relevancy. The Q² measure applies a resample technique that omits part of the data matrix and uses the model estimates to predict the omitted part. Table 6 shows the results of R² and Q² for endogenous constructs. Q² values of 0.02, 0.15 and 0.35 indicate that an exogenous construct has a small, medium or large predictive relevance for a selected endogenous construct. In addition, Table 7 shows the ƒ² effect size as indicator of the impact of a specific predictor construct (exogenous construct) on endogenous constructs (Hair et al., 2013; Rezaei and Ghodsi, 2014). The ƒ² effect size shows the change in the R² value when a specified exogenous construct is dropped from the entire model. f² and q² values of 0.02, 0.15 and 0.35 indicate a small, medium or large effect. Table 7 shows the path coefficients, f² and q².

Table 6.

Results of R² and Q² for endogenous constructs.

Endogenous construct	R ²	Q ²
Destination image	0.765	0.513
Experience	0.497	0.330
Motivation	0.576	0.385
Risk perception	0.642	0.351

Note: Q² values of 0.02, 0.15 and 0.35 indicate that an exogenous construct has a small, medium or large predictive relevance for a selected endogenous construct.

Table 7.

Path coefficients, f² and q².

Research constructs	Destination image			Experience
Research constructs	PC*	f²**	q²**	PC*	f²**	q²**
Destination image
Experience	0.436	0.275	0.156
Information sources	0.521	0.298	0.167
Motivation				0.705	0.349	0.179
Research constructs		Motivation			Risk perception
	PC*	f²**	q²**	PC*	f²**	q²**
Destination image				0.329	0.149	0.108
Experience				0.493	0.208	0.123
Information sources	0.759	0.367	0.178	0.030	0.000	0.000
Motivation

Note*: PC: path coefficients

Note**: f² and q² values of 0.02, 0.15, and 0.35 indicate that a small, medium, or large effect.

Partial least squares-multi group analysis (PLS-MGA)

Researchers indicate that comparing several groups of respondents is beneficial from a theoretical and practical perspective and failure to report heterogeneity can be a threat to PLS-SEM results as it leads to erroneous conclusions (Becker et al., 2013; Hair et al., 2012). Differences in demographic profile of IBTs can shed more light on how hypotheses between constructs may vary across demographic sub-groups. This study applies PLS-MGA using percentile bootstrapping method to examine the sub-group differences. It should be noted that the PLS software automatically discards those subsamples with little sample size such as age sub-group of 18–24, Indonesia, Turkey, China, diploma holders and other degree holders, as well as those who have visited Iran above three times.

In PLS-MGA, based on the guidelines of Henseler et al. (2009), percentages smaller than 0.05 and higher than 0.95 indicate a significant difference of a specific PLS path coefficient between groups. Therefore, a result is significant at 5% error level if the P-value is smaller than 0.05 or higher than 0.95. According to Henseler et al. (2009), the percentile below 0.05 points out that the bootstrapping results of group 1 are higher than group 2. Furthermore, percentiles higher than 0.95 indicate that the bootstrapping results of group 2 are higher than group 1. For instance, as shown in Appendix 2, examining the destination image → risk perception relationship and comparing between age group of 24–31 and 31–37, the path difference between these sub-groups is 0.495 and the p-value of 0.042 indicates that this path coefficient difference is statistically significant and the path is stronger for those IBTs between 24 and 31 years old. Information sources → motivation and motivation → experience relationships are also stronger for those IBTs between 24 and 31 years old. According to Appendix 2, the experience → destination image and information sources → destination image relationships are stronger for those IBTs above 37 years old.

Appendices 3 to 7 show the group differences among demographic sub-groups of gender, nationality, education, purpose of travelling and frequency of travelling to Iran, respectively. Bold P-values in these appendices indicate significant differences between compared groups. For example, according to Appendix 3, the path coefficient of the experience → destination image relationship is stronger for female IBTs (path coefficient = 0.275, P-value = 0.963). Interestingly, as shown in Appendix 3, the path coefficient of the information sources → destination image is stronger for male IBTs (path coefficient = 0.298, P-value = 0.015). According to Appendix 4, among the tabulated significant path differences, the path coefficients are stronger for Pakistani IBTs compared between other nationalities. Among the significant path differences, the results of Appendix 5 indicate that the path coefficients are stronger for those IBTs holding bachelor degree compared with master and PhD holders. Appendix 6 also indicates that the path coefficients are stronger for those IBTs with business meeting purposes. Finally, according to Appendix 7, among the tabulated significant path differences, the path coefficients are stronger for those IBTs who visited one time compared with two times and three times of travelling.

Discussion

Considering the financial worth and importance of business travellers in the economic growth of destinations and the impacts of different factors on their decision-making processes, this study tried to examine the relationship between risk perceptions, motivation, IS, travel experience and destination image among experienced IBTs. According to Qu et al. (2011), creating a distinct destination image has become a foundation for performing well in a competitive marketplace where most of the destinations compete intensely. Furthermore, the increasing tourism revenues globally and the intense competition have forced destinations to build favourable destination image in order to attract more visitors. Destination image is influenced by different ISs comprising of symbolic stimuli such as destination promotional information convey through media and social stimuli such as recommendation and Word of Mouth by relatives and friends. Moreover, it is also stated that individuals’ experiences at the destination may influence and modify their initial image of it and directly affect their future decision making on destination choices and future visits (Kim et al., 2012).

The result of this study found that IBTs like other travellers face common problems when travelling to overseas destinations such as unfamiliar places, concerns of being away from their workplace and home, jet lag, lack of nutrition, dehydration, stomach disorder due to having unhealthy food and finally, fear of crime and violence. Furthermore, economic recessions, terrorist activities and war threats have adverse effects on business travels and the tourism industry and more specifically each IBT responds to risk in different ways in different circumstances (Johnson, 2001; Wang, 2012). Business travellers have a long list of items that they perceive as very important when they stay at hotel such as well known and a good reputation, clean and comfortable environment, convenience to business, safety and security, wake-up calls, high-speed Internet connection, no surcharge for long distance calls and no-smoking room. Evidently there are few studies done about Iran, regarding the risk travellers’ experiences (Johnson, 2001), travel risk perceptions (Jalilvand and Samiei, 2012b; Morakabati, 2007) specifically among IBT’s risk perception in Iran. These findings are consistent and in line with Rittichainuwat (2013) who stated that if the IBTs have low sensitivity towards risk which could be due to their higher level of education as well as past travel experience, meaning that if they had a great past travel experience with the particular destination, then it could effect and minimize their risk perception.

With the 2015 negotiations between Iran and EU and removing some of the sanctions it is assumed that there will be an increase in the number of IBTs who will travel to Iran because of Iran’s high potential in business activities; therefore, the image of the country could attract more western companies or organizations to send their business travellers to participate in a variety of activities related business. Moreover, travel-related companies and organizations in Iran are providing essential services such as travel guide, translation/interpretation services with multiple languages, transfer in Iran and more importantly visa application services which could help to save time and cost effective for IBTs with regard to fulfil their needs and expectations by recruiting professional travel managers who are looking forward to serve all the requirements for travellers, specifically IBTs. Besides, we should not neglect the fact that Iran is fast developing and the country is opening up to a variety of nations globally, for all types of exchanges and business, they are all regarding actualizing and fulfilling the stereotype of magnificent culture and wonderful hospitality.

Evidence suggested that travellers are likely to utilize ISs when planning their trips such as family, relative and colleagues, destination-specific literature, news media, travel agency and travel brochures as inexpensive and versatile communication tools. In particular, another important type of ISs is brochures used by tourism services as an excellent channel for advertising. Moreover, most of the travel and tourism departments rely on commercial brochures of travel agencies and hotels (Seabra et al., 2007). Tourism researchers have investigated the motivation to travel for the better understanding and forecasting the travel behaviour. Furthermore, the motivation could be changed when an individual is travelling from one destination to another or from one activity to another during the same journey (Ingram et al., 2013). Likewise, motivation is a powerful notion, and perhaps it is not identical between two people, from market place to another, similarly from one destination to another and also from the decision-making process to another. Thus, customers have very confined motives and they are more inclined to change it from one level to the next.

Limitations of study

Like any other study, this particular research has some limitations as well as areas for further study. First, the sample size of study was limited to 234 responses with a cross sectional data analysis. In addition, attention should be drawn when examining some of our PLS-MGA results of demographic sub-groups, which had small sample size. Future research should expand the generalizability of this research by targeting more sample size following longitudinal data collection approach. Second, the study was limited to IBTs. We suggest that future studies should replicate the model (Figure 1) between various travel segments such as leisure, adventure, etc. Future studies also can apply the same concept to other Islamic destinations such as Malaysia because Malaysia is looking forward to become a business hub leader in 2020 vision.

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.

Author Biographies

Sajad Rezaei is a Lecturer in Marketing and Research Methods at Taylor's Business School, Taylor's University Lakeside Campus, Malaysia. His recent research has been published in refereed journals such as Journal of Travel & Tourism Marketing, Computers in Human Behavior, Journal of Retailing and Consumer Services, and International Journal of Retail & Distribution Management etc. His research direction has extended from his background in BSc Business Administration, MBA and doctoral dissertation with two key research themes including e-commerce and marketing management. He serves as a reviewer to several refereed journals.

Milad Kalantari Shahijan is a PhD candidate at Universiti Teknologi Malaysia (UTM). He obtained BCA from Bangalore University, India as well as MBA from Multimedia University, Malaysia. He has published his research papers in internationally refereed journals such as the Journal of Travel & Tourism Marketing and Journal of Islamic Marketing. His current research interests include travel and tourism management and consumer behavior.

Naser Valaei received his PhD from International Business School, Universiti Teknologi Malaysia. He holds Master of Business Administration in Information Technology from Multimedia University (Malaysia). His recent research has been presented at various international conferences and has been published in a variety of journals and book chapters.

Roya Rahimi is lecturer at the University of Wolverhampton, United Kingdom where as she teaches across tourism, hospitality, leisure and events subject areas. Her research interests are Customer Relationship Management (CRM), Organizational Culture, Human Resource Management, Gender Gaps and Equality and Diversity in Higher Education. Her work has been presented at various international conferences and has been published in a variety of journals and book chapters.

Wan Khairuzzaman Wan Ismail is currently the Dean of UTM International Business School, Kuala Lumpur. He obtained his BBA and MBA from Western Michigan University, USA and a PhD from Derbyshire Business School, UK. His research interests include consumer behavior, technology management and innovation, culture and leadership.

Appendix 1. Measurement items.

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