Exploring the Impact Factors of Tourists’ Intention to Choose Iran as a Traveling Destination

The purpose of this paper is to explore the effective factors in attracting outbound tourists to choose Iran as a traveling destination. This survey has been done in China. The total number of respondents was 406, where 95% of respondents filled an online questionnaire and 5% filled it manually. The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to analyze the questionnaire, and logistic regression was deployed to explore the effective factors in this survey. The questions were defined based on the theory of planned behavior (TPB) and the role of culture, custom, the source of traveling information, and perceived traveling risks in choosing Iran as a traveling destination. The outcome of this survey on Chinese people suggested that the attractions of Iran, environment, and political risks are the main factors which play an important role in choosing Iran as a traveling destination. The experience of traveling abroad also revealed a significant effect in decision making on traveling destination.


Introduction
This paper deals with exploring the impact factors causing the development of the tourism market in Iran to promote economic growth. A large percentage of economic income for the Iranian government relies on the export of oil and gas. The economy of Iran is a single product economy and some threats such as sanctions by the United States can easily affect it negatively. Indeed, it is time for the Iranian government to move toward a multi-product economy and they need to develop other industries in addition to the oil and gas industry.
One of the industries which a positive effect on the economy and countries' Gross Domestic Product (GDP) is the travel and tourism industry. Some researches such as ((Khoshnevis Yazdi, Homa Salehi, & Soheilzad, 2017) & (Mamipour & Nazari, 2014)) proved that the growth in the tourism industry has a positive effect on the rate of growth in the economy of Iran. (Khoshnevis Yazdi et al., 2017) claimed that a 1% increase in tourism expenditure (TE) leads to a 0.59% increase in real GDP in the long run.
As travel and tourism industry has both a direct and indirect contribution to the other industries such as accommodation, transportation, entertainment, and attraction; so the development in this industry causes improvement and development in the mentioned industries in response. GDP is one of the primary indicators used to gauge the health of a country's economy. The value-added amount made by services constitutes a large portion of GDP in high-economy countries suggesting that making improvement in services add more value to the income. Indeed, as travel and tourism industry is regarded as a service industry and also influences the related macroeconomic variables, the development of this industry plays an important role in the economic development (Khoshnevis Yazdi et al., 2017).
The purpose of this research is to discover the significant factors in increasing the number of incoming arrivals to Iran which directly affect the development of the travel & tourism industry in Iran. To achieve this goal, a scale was developed to measure the intention of tourists in choosing Iran as a traveling destination. A number of questions were provided to explore: What is the tourists' imagination about Iran? Which factors are important in absorbing tourists to Iran? Which factors function as deterrents in attracting tourists to Iran?

Travel & Tourism Industry in Iran
Iran is a large country located in the Middle East. Concerning the history of travel & tourism industry in Iran, it is one of the oldest civilizations in the world which was considered to be the Middle East's top tourist destination during the period 1967-1978 when Egypt was ranked 14th in the region ( (Morakabati, 2011) and (Khodadadi, 2016)). According to statistics, the number of incoming tourists to Iran in 1970 was around 243,000 which increased to 695,000 in 1978, where the rate of growth in 8 years was 2.86%, indicating a fast growth. However, this number decreased to 62,373 in 1982. What caused this collapse in the number of incoming tourists to Iran?

Source: World Bank
There are a plenty of reasons which might have caused this collapse such as the possible negative image of Iran formed by international media, 8 years' war between Iran and Iraq, no good relationship with foreign countries, political and economic sanctions, and giving a lower priority to the development of this industry by the Iranian government which can be one of the most important reasons in the decline in the number of tourists ( (Morakabati, 2011) and (Khodadadi, 2016)).

The Theory of Planned Behavior
The theory of planned behavior (TPB) model developed by Ajzen (1991) is used as one of the most recognized testing instruments for measuring the cognitive factors of consumers (Al-swidi, Rafiul Huque, Hafeez, & Mohd Shariff, 2014). This theory has been used to examine various human behaviors to predict leisure choice (Ajzen & Driver, 1992), hunting intention (Hrubes et al., 2001;Rossi & Armstrong, 1999), choice of travel destination (Bamberg, Ajzen, & Schmidt, 2003;Lam & Hsu, 2006) (Lee, 2009), ), and consumer behavior in buying organic food (Al-Swidi et al., 2014). The test of the TPB model revealed that it is a useful theoretical approach for investigating behavioral intentions (Sparks & Wen Pan, 2009).
Ajzen (2008) argued that TPB provides a sound basis to predict behavior by understanding three discrete belief categories (Sparks & Wen Pan, 2009): beliefs about some targeted behavior (such as traveling to a specific destination) as well as an evaluation of these beliefs; beliefs about the normative expectations of others (e.g. family, friends) as well as a desire to comply with these expectations; and beliefs about factors that might facilitate or impede the target behavior (e.g. available funds to travel) as well as the ability to deal with these potential impediments.
According to the TPB, people's motivation to behave in a specific way within a specific context is based on three interrelated elements which are the core of the TPB model: an individual's attitude towards the behavior (behavioral beliefs), subjective norms (normative beliefs), and perceived behavioral control (control beliefs) (Ajzen, 2005(Ajzen, , 2012 (Gstaettner, Rodger, & Lee, 2017).

Core of TPB
The theory of planned behavior (TPB) includes three core concepts which are attitude, subjective norms, and perceived control behavior. According to (Boerjan, 1974) definition, attitude consists of two elements: values and beliefs; a belief is a state of knowledge while values are learned predispositions which are shaped by enduring sources such as culture, special class, education, etc. or by transitory sources such as advertisements.
Ajzen & Fishbein (2000) have also defined attitude toward an object as a function of the belief of the object and associated implicit evaluation which occurs spontaneously and inevitably as beliefs are formed (Li, Cai, & Qiu, 2016). There are some specific definitions of attitude in the field of tourism such as tourist attitudes involving cognitive, affective, and behavioral components (Unger & Wandermman, 1985;Vincent & Thompson, 2002). The cognitive response is the evaluation made in forming an attitude; the affective response is a psychological response expressing the preference of a tourist for an entity; and the behavioral component is a verbal indication of the intention of a tourist to visit or use that entity (Lee, 2009). Attitudes toward a behavior form most favorably when individuals believe this behavior to result in beneficial and enjoyable outcomes (Gstaettner et al., 2017).
Subjective norms and perceived behavioral control were also defined by (Li et al., 2016): the subjective norm is the perceived social pressure to perform or not to perform the behavior in question, while perceived behavioral control is the difficulty of performing a behavior as perceived by the individual. The theory of planned behavior (TPB) predicts that there are some factors which potentially influence the travel intention, what others think or do (often referred to as subjective norms) as well as constraints or barriers, where the control over constraints or barriers (Ajzen, 1991) have the potential to impact travel intentions (Sparks & Wen Pan, 2009).

Sources of Traveling Information, Culture Distance, and Perceived Traveling Risks
As mentioned above, the purpose of this study is to explore the influential factors in increasing the intention of outbound tourists to visit Iran. One of the theories which is very useful in measuring the intention of customers in buying a product is the theory of planned behavior. The product of this study is traveling to Iran. Indeed, the research participants are questioned based on the three concepts of TPB which are attitude, subjective norms, and perceived behavioral control.
According to the definitions of attitude by Boerjan (1974), Ajzen & Fishbein (2000), Unger & Wandermman (1985), Vincent & Thompson (2002), Lee ( 2009), andGstaettner et al. (2017), attitude is formed by the evaluation of an object based on the knowledge associated with that object and is obviously influenced by the sociography of the evaluator, such as education, gender, age, culture, and so on. In the tourism field, knowledge can be acquired from different sources of information about a specific destination. Books, articles, media, newspapers, journals, tourism websites, families, friends, tourists' own traveling experience, and travel agencies are some examples of the sources for information collection. These sources are very important as tourists' decisions might be affected by them positively or negatively. It also has influence on forming the tourists' expectations which is one of the important elements in purchasing decision (Alvarez & Korzay, 2008).
Tourists' attitude and tourists' image are very similar by definition; tourists' image has been defined by Barich & Kotler (1991) as the sum of beliefs, attitudes, and impressions a person or group has of an object where impressions may be true or false, real or imagined (Rajesh, 2013). Therefore, there is no difference in the influence of the information collection sources on them. Fakeye & Crompton (1991), and Gunn (1972) defined three types of tourists' images based on the sources of acquiring traveling knowledge (Byon & Zhang, 2010); organic image originates from non-tourism information such as geography books, television reports, or magazine, and articles; an induced image can arise from tourism-specific information such as a destination brochure or vacation website; and complex image can be a result of direct experience of the destination (Fakeye & Crompton, 1991).
Based on the outcomes of Frederik et al. (2016) research, access, amenities, and local community are the other issues which have a positive significant impact on tourists' attitude (Frederik, Brunner-sperdin, & Stokburger-sauer, 2016). Several studies have examined the role of cultural distance, what represents the extent of cultural discrepancy between tourists' home and destination countries (Ng, Lee, & Soutar, 2007), and its association with tourist destination choice (Jackson, 2000(Jackson, , 2001Ng et al., 2007;Ng, Lee, & Soutar, 2009;Vietze, 2012;Yang, Liu, & Li, 2016;Yang & Wong, 2012). Some have concluded that cultural distance negatively impacts destination choice, such that tourists are more likely to visit destinations that are culturally similar to their home countries (Jackson, 2000;Ng et al., 2007Ng et al., , 2009Vietze, 2012;Yang & Wong, 2012) while some have found mixed results pertaining to the relationship between cultural distance and destination choice (Jackson, 2001;Yang et al., 2016) (Liu, Robert, Cá rdenas, & Yang, 2018). It seems that as the cultural distance can be one of the motivators for absorbing outbound tourists, it can also be concerned as perceived barriers, challenges, and conflicts for some outbound tourists in choosing a traveling destination. For example, in 2010, the British Broadcasting Corporation (BBC) reported that a British man and woman in Dubai were fined for drinking alcohol and sentenced to jail for kissing in public (BBC, 2010) (Liu et al., 2018).
Based on the in-depth literature review, the respondents were questioned about the cognitive of the country image for measuring the tourists' attitude (Table 4), which includes different questions associated with the Iranian community, culture, attractions, accessibilities, and amenities. Each item was phrased into a five-point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree. Several questions were also defined to ask about the sources of gathering traveling information (Table 4) which are important in shaping the tourists' attitude. Each item was phrased into a five-point Likert scale, ranging from 1 = not important to 5 = very important.
The TPB predicts that there is a range of factors that can potentially influence or constrain travel intentions (Sparks & Wen Pan, 2009). Subjective norms are one of the factors which are very important in the decision-making process of. It is shaped by social influences and beliefs. What do other people think about your decision or behavior? Where do the important people to you recommend you to travel? Which destination is popular among my family, relatives, friends, and colleagues for traveling? As noted in the previous section, these types of questions and their answers can directly influence the traveling intention. Some constraints or barriers easily change the tourists' intention of traveling to a place. For example, traveling cost and time are two factors that directly affect tourists' behavior in choosing a traveling destination. The other important factor which is captured by perceived behavioral control is the risk and uncertainty associated with the traveling destination (Floyd et al. 2004;Fuchs and Reichel 2006;Kozak, Crotts, and Law 2007;Sönmez andGraefe 1998a, 1998b;Karl, 2016 (Williams & Balaz, 2013).
Individuals perceive, evaluate, and respond to risk in a variety of ways, depending on psychological processes and the perceived situational context at the time of making a decision ((Trimpop, 1994)& (Gstaettner et al., 2017)). If the difference between risk perceptions/cost and the attractiveness of a destination has a strong positive value, the individual might decide to travel to that destination (Korstanje, 2009). The reaction to the outcome of assessment is different, where tourists may decide to travel to a different destination and find a substitute for an alternate destination ((Decrop, 2010)& (Karl, 2016)), alternatively, tourists may choose to travel to the same destination but later, or alter their travel plans by shifting from traveling individually to booking a package tour, or from traveling alone to traveling in groups (Adam, 2015).
Note that peoples' perception of risk is somehow variable, where this understanding could be influenced by factors such as the media, their social surroundings like friends, families, tourist organizations, their personalities, and their past experience (Korstanje, 2009). The result of Kozak, Crotts, and Law (2007) research demonstrated that participants identified risk of infectious illnesses as a major one and they cataloged the risk of terrorism in a secondary role and they also mentioned that negative risk perceptions not only affect involved countries but also neighboring ones or broader regions (Korstanje, 2009).
Several questions related to subjective norms, perceived behavioral control, perceived local barriers, and perceived traveling risks and uncertainty, based on the in-depth review of ( (Morakabati, 2011); (Karl, 2016) and (Reza Jalilvand & Samiei, 2012)) research, were defined in the questionnaire (Table 4). Each item was phrased into a five-point Likert scale, ranging from 1 = not important to 5 = very important.

Methodology
According to the theory of planned behavior (TPB), there are three main concepts which are attitude, subjective norms (social pressures), and perceived behavioral control. The purpose of this research is to measure the intention of outbound tourists to choose Iran as a traveling destination, where TPB has been employed as a fundamental theory in designing the scale for this research.
This research has been conducted in China. The participants in this study were Chinese people who were older than 18 years old. There was no other limitation for choosing respondents in this research. There were two reasons for choosing Chinese people in this study: first, because of administration of this survey in China, and due to the growth rate of outbound Chinese tourists. The number of outbound trips from China has reached 129 million in 2017, up to 5.7% greater than 122 million in 2016 ("2017 China Outbound Tourism Travel Report," 2018). Further, based on this report, the top 10 popular destinations with the fastest growth in attracting the Chinese tourists' attention were Turkey, Germany, Vietnam, Spain, the UAE, Italy, Philippines, Australia, France, and Egypt in 2017. Turkey is the neighboring country of Iran and there are some similarities between Iran and Turkey especially in their culture. If Turkey is an interesting traveling destination for outbound Chinese tourists, it will be a good sign for the tourism market in Iran to attract them as well.
The research participants were 406 subjects who were asked about their attitude about the specific destination which was Iran. Other questions addressed the source of gathering information for choosing a traveling destination, as it is one of the important factors in forming the tourist attitude, subjective norms, and perceived behavioral control which were proved to have a direct effect on the tourists' behavior in choosing a place as traveling destination (Sparks & Wen Pan, 2009).
It is not easy to know the accurate proper sample size for factor analysis. According to a number of textbooks, for example, Gorsuch RL. (1983), Tabachnick BG, Fidell LS., 2007Hair et al. (1995), Pett MA, Lackey NR, Sullivan JJ. (2003), cited the work of Comrey and Lee (1973) in their guide to sample sizes: 100 as poor, 200 as fair, 300 as good, 500 as very good, and 1000 or more as excellent ( (Williams, Onsman, & Brown, 2010), (Wilson Van Voorhis & Morgan, 2007), and (Henson & Roberts, 2006)). In this study, there were 406 participants as the research sample, which is better than good based on Comrey and Lee (1973).
After preparing the questions based on the literature reviews, the preliminary questionnaire was sent to two university professors, one specializing in tourism and service innovation and the other in research methods in social sciences along with three experts in business marketing. The modified questionnaire was translated to Chinese and it was sent to a group of 30 people, who were professional in Chinese and English language, as a pilot study where they were also asked to mention the translation issues. The final questionnaire was bilingual in English and Chinese with 95% of data collection being online and 5% manually. The IP address of participants, who filled out the online questionnaire, were recorded in the database, so everyone was allowed to participate in this survey only once.
SPSS statistical version 23 was used for descriptive analysis while exploratory factor analysis (EFA) and Amos software were used in confirmatory factor analysis (CFA). As mentioned by (Hinkin, 1995), various scholars suggest using both EFA and CFA when developing a new scale (Byon & Zhang, 2010); so both EFA and CFA were used in this study.
The following steps were taken to analyze the preliminary questionnaire in this research: 1. calculating Cronbach's coefficient alpha; 2. analyzing the validity through construct validity plus content validity; 3. exploratory factor analysis; and 4. confirmatory factor analysis.
After evaluating the scale, as there were one dependent variable and some independent variables, the logistic regression was employed to predict the impact of each categorical independent variable on the dependent variables. Further, as the type of dependent variable was binary, so binomial logistic regression (BLR) was employed in this research. The next step is to check for missing data (Schafer & Graham, 2002); there were only 15 cases which had no data in two questions. The missing data were completed by replacing the mean value of those questions in empty cells; as it was mentioned by Paul Kline that the mean value will not make any changes in the distribution model.

The Refinement of the Scale
According to (Tucker & MacCallum, 1997), the factor analysis methodology is used to determine the number and nature of the factors, as well as the pattern of their influences on the surface attributes. The exploratory factor analysis was used to refine the preliminary scale. The alpha Cronbach of all the items was calculated. The Cronbach's alpha was 0.934 for 45 items. As it was greater than 0.7, it can be said that the reliability of the scale is high. The value of "Cronbach's alpha if item deleted" for all the items was less than "Cronbach's Alpha Based on Standardized Items" which was 0.941, so there was no need to omit any items at this step.
The KMO (Kaiser-Meyer-Olkin) and Bartlett's test was done to understand if it is possible to reduce the large number of 45-scale items into fewer factors. As observed in Table 4, the result of KMO test is 0.914, suggesting that it is possible to cluster the 45 items in the scale into fewer factors. The Bartlett's test of sphericity equals to 10943.394 and it is significant at the level of p<0.01 which means that despite having a strong correlation between variables in each factor, there is no correlation between variables of the different factors. .000 The principal component analysis, Scree test, and Varimax rotation were run to find the number of significant factors. For the Kaiser's criterion which is a kind of rule of thumb, eigenvalue greater than 1 (Kaiser HF., 1960) (Williams et al., 2010) and the criterion of factor loadings greater than 0.3 were considered in this study. It is suggested to use the Scree test in conjunction with Kaiser rule for determining the retaining factors (Yong & Pearce, 2013).
The table of "total variance explained", Table 5, shows that there are ten items with eigenvalue greater than one. The outcome of eigenvalues and Scree plot indicate that the items can be clustered into ten significant factors. The varimax rotation method was utilized to cluster the items into ten factors. The factors will be named conventionally as there is no rule for naming these factors or latent variables. Each factor includes a number of items, where the one with only two items is not a strong factor.

The Validation of the Constructs
Amos was employed to validate the constructs derived from EFA and Alpha Cronbach's tests. The 45-scale items with first-order factor structure were tested. The output of the test is shown in Table 6. This model seems to require modification as the coefficient value of determination (R 2 ) for several indicators were lower than 0.5. Based on the recommendation of Hair et al. (1998), such variables should be deleted from the model (Ho & Lee, 2007). Based on the recommendation of Hair et al. (1998), seventeen indicators with a value lower than 0.5 were omitted, after which confirmatory factor analysis was run again. The results of this analysis are shown in Table 7.
The evaluation of the model was done in this phase. It is not very easy to evaluate the fitness of a model to the data, while several indicators are going to be checked. The value of CMIN/DF (X 2 / degree of freedom) was checked as the first indicator of a good model fit. According to (Ghasemi, 2014), the value of this indicator can range from 1 to 5 and the value which is near 2 or 3 is interpreted as a very good model fit to the data. As observed in Table 7, the value of CMIN/DF decreased from 3.093 to 2.761 which was mentioned as a good model fit.
The other two indices which estimated the improvement in fit were the Tucker-Lewis Index (TLI; also known as the Non-normed Fit Index) and the Comparative Fit Index (CFI). Both of these statistics were bound between 0 and 1, where Monte Carlo research suggests that values of .95 or higher indicate a good model fit. The value of TLI (table 7) increased from 0.801 to 0.903, and the value of CFI increased from 0.819 to 0.920 revealing a fairly good model fit.
GFI (Goodness-of-Fit Index) is another indicator in evaluating the model fit to the data. According to Jӧreskog & Sӧrbom (1984), the value of GFI is equal or less than one and if it equals to one, it means that the model completely fits the data. The value of GFI (  (2010), if the value of RMSEA is equal to or less than 0.05, it will be an excellent fit model, while if it is between 0.05 and 0.08, it will have an acceptable goodness of fit (Movahed Mohammadi & Pouratashi, 2016). The value of RMSEA (Table 7) was reduced from 0.072 to 0.066 suggesting an acceptable fitness of the model.

Binomial Logistic Regression
Choosing or not choosing Iran as a traveling destination was the dependent variable in this study. The independent variables were categorized into two groups: sociodemographic variables and intention affected variables. The sociodemographic variables included gender, age, religion, traveling budget, number of traveling abroad, and travel partner. On the other hand, the intention affected variables involved the variables which were finalized in the CFA test. As shown in Table 8, traveling abroad once, attractions of Iran, the environment of Iran, and political risks were significant with P<0.01 and P<0.05. Based on this outcome, it could be interpreted that the probability of choosing Iran as a traveling destination by Chinese tourists depends on the mentioned independent variables.
As observed in Table 8, attractions of Iran along with its environment had a positive impact on choosing Iran (β coefficient in BLR), while the other two variables which were political risks and traveling abroad once had a negative impact on choosing Iran. To explain it comprehensively, it means that improving the attractions of Iran by one unit leads to about 55% increase in the intention of Chinese tourists in choosing Iran (according to the value of Exp(β)). More importantly, improving the environment of Iran by one unit results in around 203.3% rise in Chinese tourists' intention. Finally, decreasing the perceived traveling risks by one unit causes an increase in Chinese tourists' intention in choosing Iran by about 74.9%.

Model Evaluation
Figures in Table 9 demonstrate the fitness of the model. The value of (-2log likelihood) equals to 271.031. The value of Cox & Snell R Square equals to 0.513, this indicator plays the same role of R 2 in regression analysis and if it is greater than 0.50, the fitness of the model will be good. The next indicator is Negelkerke R Square, whose value also confirms this model goodness of fit.
The next test is based on the chi-square distribution (X 2 ) and the significance of the model, which is the Hosmer & Lemeshow test. The H0 hypothesis in this test suggests lack of a relationship between independent and dependent variables or all of the regression coefficients equal 0. This hypothesis is rejected in the confidence level of 99% as it is significant in p<0.01. To confirm this result, there is another indicator in regression logistic which is called the power of the detection model. The percentage of correctness reveals the correctness of the model in logistic regression. According to Table 10, the percentage of correctness for this model has been 87.7% which is a good value for this indicator.

Conclusion
Based on the results of the Binomial Logistic Regression, there are three factors whose improvement might have a more positive influence on the Chinese intention in choosing Iran as a traveling destination. There are factors related to the attractions and environment of Iran and political issues. Attractions of Iran include historical and cultural sites, world heritages, holy shrines, natural sightseeings, handicrafts, and adventurous activities. The number of the cultural world heritages in Iran such as Bisotun, Golestan Palace, and Persepolis, which are already registered by the United Nations Educational, Scientific and Cultural Organization (UNESCO), are 22 sites and there is only one natural world heritage in Iran which is Lut desert (List, 2019).
Previous research about the role of world heritage sites in absorbing outbound tourists by (Su & Lin, 2014) proved that both cultural and natural world heritage sites could enhance the number of inbound tourists, but the effect of natural world heritage sites is slightly larger than that of cultural heritage. Another research on Chinese tourist's behavior on destination choice by the authors also found that Chinese tourists have more interest in visiting natural sightseeing. So improving attractions in this study could be mostly related to nature-associated issues. Another research also claimed that tourists have more motivation for experiencing new unique activities (Frederik et al., 2016). These research achievements guide tourism operators to concentrate more on providing better facilities for visiting natural and cultural sites in Iran and designing more unique activities. For example, the process of making rose water in Kashan could be useful in improving this factor.
The environment of Iran includes the characteristics of Iranian people and the safety of Iran. Most outbound tourists mentioned the hospitality and the friendly behavior of Iranian people in treating them. They were very impressed by such moral characters. It can be pointed out that these attributes might play an important role in making outbound tourists feel more attached to a specific destination which will influence developing the tourist market in the mentioned destination (Lee, 2009). Indeed, presenting the reality of people's life and safety of traveling in Iran could enhance the interest of outbound tourists to choose Iran for traveling.
Another impacting factor in decision-making by Chinese tourists is the issues related to political unrest and the relationship between Iran and western countries. Nevertheless, according to Anholts (2002), a distinction should be drawn between a country image and destination image as the country image is the sum of beliefs regarding a country and is affected by economic, political, and geographical factors while destination image is related to how a country is perceived as a vacation place (Lee, 2009 Similarly, of the number of tourists fell in Turkey recently because of the decisions made by the government based on political affairs. So, improvement in this issue will be effective in enhancing the tourists' intention to choose Iran as a traveling destination.
Another factor which revealed negative effects on the Chinese tourists in choosing Iran as a traveling destination was the number of trips abroad. The people with one experience of traveling abroad had a negative intention for traveling to Iran. It could be interpreted that traveling to Iran is not in priority for Chinese people, and Iran is not attractive enough for them. It is suggested that the correct information should be provided to inform Chinese tourists about Iran by making short videos of the attractions of Iran, presenting Iran in tourism fairs, and publishing guide books about Iranian culture, history, geography, and all the issues related to tourism.
There are several limitations to this research. It was done in China. As such, It is suggested that this survey be conducted in other countries to measure the intention of people in choosing Iran as a traveling destination. Knowing the perception of tourists about Iran would help to improve tourism affairs in this country. Secondly, it focused mostly on Chinese people who were living in Shanghai. It would have been nice if this survey had been taken in other parts of China as well, as China is a vast country, to obtain far more accurate information about Chinese outbound tourists.