In a globalized society, the relationship between neighboring countries and those inhabiting them is important. Asia is the largest continent and consists of people from many different cultures. Understanding the attitudes and potential future interactions between members of these societies and what drives these interactions is important to understanding and anticipating the future. A survey of young people, particularly university students, who are likely the potential future leaders of their respective countries, is one way to assess potential future interactions between countries. We were interested in understanding what factors affected their likelihood of visiting particular cities in various Asian countries, as well as exploring the potential policy implications for policies that could improve these relationships.

Literature Review

Mere Exposure

The mere exposure effect suggests that the more we are exposed to something, the more we like it (Zajonc, 1968 as cited in Bornstein & Craver-Lemley, 2022). Others have also looked at the relationship between the mere exposure effect and tourism, but focused primarily on advertisements (Coşkun, 2022). Kim et al. (2019) found that destination familiarity positively affects both cognitive and affective image of a location and increases likelihood of traveling to that destination.

Personal Exposure

A study of tourists in Greece used mere exposure theory, as well as contact theory, to explain how interactions with local residents affected tourists’ decision to return to a particular destination (Stylidis, 2022). A survey of tourists visiting Ho Chi Minh City, Vietnam found a relationship between city image and their intention to visit again (Phi et al., 2022).

On the other hand, Putri and Yasri (2020) found that word of mouth had no effect on destination image or decision to visit the city of Padang, Indonesia.

Media Exposure

The effect of various forms of popular culture on travel destination decisions has been explored to some extent. Some governments have sponsored films with the intention of improving city image and attracting visitors, including in the cities of Taipei and Kaohsiung, Taiwan and Hong Kong (Chen, 2019). Films have been suggested as a way of creating a brand and increasing tourism in a city, like Hong Kong (Y. Liu, 2023). While it has been argued that tax incentives for filmmakers in states in the US have little positive effect on local economies (Button, 2019), the long term effects of the increased exposure these films cause has not been studied sufficiently. In addition, while even a negative portrayal of a location in a film can have a positive impact on tourism, the overall economic effect can still be negative (Pratt, 2015).

A survey of 220 young adults in Hong Kong found that visiting the destination where a film took place was not recognized a motivator for most respondents, although there was some interest in visiting film locations if they happened to be visiting an area (Ng & Chan, 2020). Similarly, Vila et al. (2021) looked at both familiarity and visit motivation of domestic tourists visiting the filming location in Spain and found both factors to be important, but familiarity to be more important. Viewing a reality TV show featuring international destinations was also found to have a positive impact on cognitive and affective destination image and familiarity, which in turn, increased intention to travel to the location (Mege & Aruan, 2017).

Social Media

The effect of user-generated content, both the kind found on social networking sites and review sites, on destination choice has been studied (Zhang et al., 2021). One study from Indonesia found that social media had a positive effect on the image of the city of Padang, but no direct effect on choosing it as a tourist destination, even though city image did affect destination choice (Putri & Yasri, 2020). A survey of Malaysian people regarding a city in Saudi Arabia found that social media had a moderating effect, strengthening the relationship between perceived destination image and travel intention (Al-Gasawneh & Al-Adamat, 2020).

Some studies looked at particular kinds of social media. Another survey from Indonesia found electronic word of mouth on Instagram positively affects destination image, which mediates the relationship between social media and travel decisions (Fatmawati et al., 2022). Tik Tok videos were also found to affect destination image and tourism intention (Xiao et al., 2020).

Social media marketing from the Batam City, Indonesia hospitality industry was found to have a positive effect on purchase intention, including through e-word of mouth (Putra & Aprilson, 2022). User generated content and Google images of Mexico City were found to improve the image of Mexico City than images created by a destination marketing organization (Bernkopf & Nixon, 2019), contradicting the findings of Marder et al. (2019) that people prefer professional photographs over amateur aesthetics of a location, increasing their intention to travel there.

Online reviews related to tourism are not always helpful, but there are some determinants about the review, particularly review length, that make them more helpful; some determinants, like valence (i.e., whether it is positive or negative), did not have an effect (Hu & Yang, 2020). Although online review sites are not exactly the same as some other forms of social media, these findings support a model where increased exposure (i.e., longer reviews) is a more important factor than the attitude expressed therein (valence) and suggests that further exploration of the mere exposure effect of user-generated content found on social media is warranted.


The relationship between familiarity and travel has been examined in some extent by others. For example, Liu et al. (2018) studied the impact of familiarity and geographic distance on perceived cultural distance, the impact of perceived cultural distance on international travel destination choice for residents of mainland China. Although they found that perceived cultural distance didn’t have an effect on travel destination choice, they did find that both familiarity and geographic distance affected perceived cultural distance. As mentioned above, Vila et al. (2021) found familiarity to be the strongest factor they considered among those choosing to visit filming sites in Spain. We proposed that familiarity itself would influence travel intention and that it would also be mediated by exposure.

City Image

Destination image has been found to be strongly related to travel destination decisions, including among young German travelers to ASEAN countries (Promsivapallop & Kannaovakun, 2017).

In conclusion, the decision to visit a city has been found to be related to both familiarity with the city as well as to city image. Various types of exposure, including personal, media, and social media have also been found to influence intention to visit.

Hypotheses and Model Building

Drawing on mere exposure theory, as well as other factors sometimes associated with the decision to visit a city, like familiarity and city image, and wanting to explore the path of exposure to intention to visit, we proposed the following hypotheses:

H1: Exposure to a city is positively correlated with intention to visit that city.

H2: Exposure to a city is positively correlated with intention to visit a city through self-perceived familiarity with that city.

H3: Exposure to a city is positively correlated with intention to visit a city through city image of that city.

H4: Exposure to a city is positively correlated with intention to visit a city through self-perceived familiarity and city image.

These first four hypotheses are illustrated in Figure 1.

Figure 1
Figure 1.Paths of Hypotheses

In addition, understanding that some forms of exposure may have a stronger effect than others, we proposed an additional hypothesis regarding this:

H5: Personal exposure to a city will have a stronger effect on the intention to visit compared to exposure through other channels like media and social media.


Questionnaire Design and Variable Measurement


Since it would be difficult for respondents to accurately remember or quantify their exposure to a particular city, the exposure variable was measured by how many of the seven exposure methods (visited it myself, someone I know visited/lives there, social media, traditional media [newspaper, TV news, radio, etc.], TV/movie that takes place or references this city, music, learned about it in a class/academic text) a respondent had ever experienced for each city.

To test H5, we broke the types of exposure into three categories: personal exposure (visited it myself and someone I know visited/lives there), media (including news media, TV/movies, music, and educational activities), and social media. Social media, depending on how it is used, could be similar to personal exposure, like when real life friends share information about their trips, or similar to traditional media, like when a social media user follows the accounts of celebrities or traditional media. So, it seemed best to put it in its own category.


Familiarity was measured by using a scale of 1-6 for each city, where 1 = never heard of it and 6 = know it very well. Assessing the accuracy of respondents’ self-assessed familiarity was not necessary since we were not interested in their actual knowledge but their perceived familiarity relative to their perceived familiarity with the other cities.

City Image

Respondents were asked to identify which of 15 attributes a city has. Of the city attributes used on our questionnaire, 13 were from Dolnicar & Grun (2012). However, since Dolnicar and Grun’s study was primarily about attitudes related to tourist destinations and we were interested in all types of travel, including business, we added two additional city attributes that we believed would be important in business: “economic center” and “technological center.” The number of attributes a respondent assigned to a city were then summed to make the city image variable for analysis. Two of the city image attributes (crowded and expensive) could be seen as negative, which might suggest that if a city has them, it would be negatively correlated with intention to visit and that a city not having them would be better. We considered whether we needed to reverse code (i.e., change a score of 6 to a score of 1, a score of 2 to 5, etc., but re-coding these answers did not seem to make a difference in the analysis, so ultimately we did not re-code these answers. This may be because a desirable city is also likely to be crowded and expensive.

Intention to Visit

For the final dependent variable in our model, intention to visit a city, respondents were asked how likely they were (on a scale of 1-6 where 1 = definitely not and 6 = definitely) to visit each of the city for each of three purposes: tourism, business, or other reasons (e.g., to visit friends, to study, etc.). In creating our main model, the total score for each respondent for each city was summed.


The population of this survey was young university students. Because we assumed, as some others have found (e.g., Czepkiewicz et al., 2020; Zhao & Yuan, 2023), those with a higher level of education are more likely to travel internationally for business and for pleasure, that it was reasonable to focus on this group.

One professor in each of seven countries was asked to field the survey in their country, including translation of the survey into the local language from English if needed and testing their translation. In Korea, a commercial panel was used to collect some of the responses. In others, the professor used their own connection to students and their professional network to invite respondents to complete the survey (see Table 1). All surveys were conducted online from September to December 2023. No partial questionnaires were included in the analysis. Additionally, some questionnaires were excluded. First, as this was a survey of young people, the ASEAN definition of youth (ages 15-34) was used; 94 cases excluded due to the respondent falling outside of that age range. The actual age range of included respondents was 17-32. Some (186) completed questionnaires were excluded as lacking quality if the respondent indicated they had never heard of a city, but then subsequently said they had visited that city. A total of 3,596 valid responses were included in our analysis. See Table 2 for the gender, age, and country of residence distribution of these respondents.

Table 1.Sampling Methodology and Language
Country Recruitment method (n) Language
China Snowball sampling (students at 1 university were asked to complete survey and invite students from other universities) Mandarin
India Personal network within a province English
Indonesia Students from several universities via professional network of professor Indonesian
Korea Students in class at 1 university (220)
Online panel (300)
Malaysia Students at 3 universities (291) English
Philippines Personal network in metro Manila; approximately 30 universities were included (1111) English
Thailand Students at 8 universities (809) Thai/English
Table 2.Demographics of Respondents (N = 3,596)
Variable n %
Age 17 44 1.2
18 688 19.1
19 715 19.9
20 556 15.5
21 530 14.7
22 397 11.0
23 212 5.9
24 146 4.1
25 105 2.9
26 56 1.6
27 47 1.3
28 46 1.3
29 22 0.6
30 10 0.3
31 12 0.3
32 10 0.3
Total 3596 100.0
Gender Male 1421 39.5
Female 2086 58.0
Other 89 2.5
Total 3596 100.0
Country of University China 313 8.7
India 107 3.0
Indonesia 435 12.1
Korea 520 14.5
Malaysia 283 7.9
Philippines 1111 30.9
Thailand 799 22.2
Other 28 0.8
Total 3596 100.0

Although the number of respondents from each country was not the same, no weighting was applied. As our interest was in the relationship between the variables and not the desire to visit any particular city, this seemed to be the simplest course of action. If anyone wanted to do a secondary analysis of this data to identify more absolute information, like the most desirable travel in Asia, weighting based on country of respondent would be necessary. Respondents were more likely to say that they would visit the city or cities in their country of residence. So, for example, having more respondents from the Philippines may affect the apparent popularity of cities in the Philippines.

No weighting was applied based on age because the majority of respondents were the traditional university age, so no real age-related differences were expected. There were more female respondents than male, but since we do not know the exact distribution of the gender of university students in each country, weighting by gender was not practical either. In addition, gender-based weighting did not seem necessary, because gender does not moderate mere exposure effect (Bornstein & Craver-Lemley, 2022). Finally, we were interested in the relative effects of exposure to intention to visit a particular city, with the mediating effects of city image and familiarity, not the absolute effects that could make weighting necessary.


Hayes (2018) created macros for conducting regression-based mediation, moderation, and conditional process analysis, called PROCESS; he developed several models for various mediating and moderating models. The hypotheses were tested using Hayes’ (2018) PROCESS macro Model 6, which is designed for testing the direct and indirect effects of one predictor, two mediators, and one outcome, that is to test models that are similar to ours (see Figure 1). We used SPSS V26 Each respondent’s answers about a single city was considered one case in our analysis, meaning a single respondent represented 14 cases in the analysis.



As predicted, exposure affects intention to visit both directly and through the mediating variables familiarity, city image, and through both familiarity and city image. However, the latter effect size is very small (.018; see Table 3), showing a weaker effect than the other paths. Although the direct effect of exposure is statistically significant (p < .001), the size of the effect is relatively small (.099). The biggest mediating factor is familiarity (.172), that is, exposure makes people more familiar with a city, which increases their intention to visit the city (see Table 3). H1-H4 were supported by this model.

Table 3.Effect of Exposure on Intention to Visit Based on Type of Exposure (N = 3,596): Results of Hayes’ PROCESS Macro (Model 6)
Path of the Effect Effect se t p
All Types of Exposure Total effect of exposure on intention to visit .380 0.004 88.05 <.001
Direct effect .099 0.005 20.36 <.001
Indirect (through familiarity) .172
Indirect (through city image) .091
Indirect (through familiarity and city image) .018
Personal Exposure Total effect of exposure on intention to visit .941 0.011 85.38 <.001
Direct effect .336 0.011 29.28 <.001
Indirect (through familiarity) .396
Indirect (through city image) .143
Indirect (through familiarity and city image) .067
Media Exposure Total effect of exposure on intention to visit .439 0.006 70.36 <.001
Direct effect .083 0.006 12.99 <.001
Indirect (through familiarity) .213
Indirect (through city image) .110
Indirect (through familiarity and city image) .032
Social Media Exposure Total effect of exposure on intention to visit .119 0.014 8.74 <.001
Direct effect -.053 0.006 -4.48 <.001
Indirect (through familiarity) .051
Indirect (through city image) .109
Indirect (through familiarity and city image) .012

To test H5, we broke the types of exposure into three categories. Personal exposure had a much larger effect than the other kinds of exposure (.941); it also had the largest direct effect (.336). It continued to follow the pattern of overall exposure, with the mediating role of familiarity having the greatest effect (.396). The effect of media exposure was less, but still significant. Familiarity as a mediating variable was also the greatest part of this effect (.212), but here city image (.110) had a greater effect than exposure itself (.083). Although the effect of social media was not as strong as the other kinds of exposure, it was still statistically significant. In addition, city image played a greater role than familiarity as a mediating variable when exposure was just through social media. Interestingly, the direct effect of social media exposure on intention to visit a city was negative. These differences in the strength of the effect for each type of exposure mean that H5 was also supported.

Differences Depending on Type of Travel

Because the attributes that make a city attractive for business or for tourism might be different, and so the strength of the city image variable in our model might vary if only the relevant attributes for each type of visit were considered, we re-tested our model using tourism-related attributes with intention to visit for tourism and business-related attributes with intention to visit for business. The attributes were categorized using factor analysis. One attribute, “easy to travel,” was found to apply to both pleasure and business and so was included in both components. If an attribute was > .350, it was considered to be part of the component. There were eleven tourism attributes and five business attributes (see Table 4).

Table 4.Rotated Factor Structure of City Attributes (Varimax Rotation)
Component 1 (Pleasure) Component 2 (Business)
Offers many activities for tourists .367 .198
Authentic .392 .184
Clean .574 .113
Has a Good climate .603 .058
Crowded .006 .638
Has cultural attractions .412 .315
Easy to travel .451 .441
Exciting .516 .299
Expensive .232 .588
Friendly .601 .216
Has a beautiful natural environment .685 .067
Relaxing .713 .121
Safe .600 .282
Economic center .215 .727
Technological center .216 .682

H1-H4 are still supported in both the tourism and business travel intention models, although the size of the effect is greater on the intention to visit for pleasure (see Table 5). The city image attributes were more important for pleasure visits than for business visits.

Table 5.Intention to Visit Based on Purpose with Related Attributes
Path of Effect Effect se t p
Intention to visit for pleasure Total Effect of Exposure on Intention to visit for pleasure .422 0.005 82.46 <.001
Direct Effect .105 0.006 18.29 <.001
Indirect (through familiarity) .182
Indirect (through city image) .113
Indirect (through familiarity and city image) .023
Intention to visit for business Total Effect of Exposure on Intention to visit for business .334 0.005 68.12 <.001
Direct Effect .102 0.006 17.98 <.001
Indirect (through familiarity) .165
Indirect (through city image) .057
Indirect (through familiarity and city image) .011


Types of Exposure

All of the hypotheses were supported by our research, although some of the relationships were weaker than others. Our findings regarding familiarity, image, and intention to visit were consistent with the findings of others (e.g., Kim et al., 2019). While all types of exposure had a statistically significant (p < .001) on intention to visit, personal exposure, that is, having been to a city or knowing someone who has been to or lived in a city, had a much larger effect (both direct and indirect through familiarity, city image, and both) on intention to visit than the other types of exposure. In addition, the direct effect of personal exposure on intention to visit was much higher (.336) than the direct effect of media exposure (.083). The weakest form of exposure was social media exposure with a total effect size of only .119. In addition, the direct effect of social media exposure was actually a negative effect, that is, being exposed to a city on social media may make people less likely to visit a city. These findings were different from the findings of Putri and Yasri (2020) who did not find a relationship between word of mouth and city image/intention to visit, but who did find one with social media. It also contradicts the social media effect found by Putra and Aprilson (2022). Our results may be related to the observations of Marder et al. (2019) regarding people’s reaction to amateur aesthetics in photographs compared to their reactions to professional photographs of a location. Exposure on social media may be largely exposure to non-professional photographers’ photographs of a location, which may actually make the location look less appealing and therefore make them less likely to visit unless other mediating factors are present.

Limitations and Directions for Future Study

One limitation of this survey is the sampling methodology, as convenience sampling was used in most of the countries. However, as the target population was young university students and our interest was in the relationship between the variables rather than absolute desire to visit a particular location, this method was well-suited for our purposes. Of course, repeating the study with a probability-based sample in the future would be desirable, as would the inclusion of additional countries in the study.

Due to some ambiguity about what the term “social media” may mean to respondents in various countries, future studies might want to clarify this term or add additional terms. We have no way of knowing whether respondents were primarily imagining social media posts from people they personally knew, social media accounts run by governments or corporations, or other non-personal but user-based content, like review sites. The reactions to these different types of social media may not be consistent. For this reason, caution should be used in interpreting our results related to social media.

There are also many other types and sub-types of exposure that might be considered in future studies. For example, we did not consider direct advertising and cannot comment on the relative efficacy of advertisements compared to other forms of exposure. We also did not consider whether actual improvements to a city in terms of, for example, more efficient transportation, beautification projects, or improvements in safety may affect city image. Of course, city planners must consider the necessity of these other policies in addition to considering how images are created.


Theoretical Implications for Mere Exposure Theory

Our study supports the existence of the mere exposure effect in influencing young, educated people’s intention to travel to a particular destination. For example, popular TV shows or music referencing a city can make people more familiar with the city and thereby more likely to visit it. However, personal exposure was an even stronger predictor of intention than media exposure. In short, exposing people to something will make them more familiar with it, improve their image of it, and ultimately, increase their intended engagement.

Potential Policy Implications

As cities, regions, and countries attempt to improve the image of a location and encourage visitors to come to the city, they may be faced with various options. Our results suggest that any policy which increases the amount of exposure of the city will ultimately be beneficial and that increasing the channels of potential exposure to a city may increase its popularity as a destination. For example, regardless of specific, measurable effects of a particular film or television program, the mere exposure that the film generates may be assumed to be beneficial to the city, suggesting a justification for continuing or creating policies encouraging creators to make films, etc. in a particular city. While media, by its nature, may be able to reach greater numbers of people, our results show that personal connections are even more powerful, suggesting that forming personal connections may be the most effective strategy in facilitating international cooperation in Asia in the future as well as improving city image and increasing future visits for business and pleasure. Government entities looking to foster this personal connection as part of their policy can accomplish this in part with grants helping to fund study abroad or exchange student programs as well as international conferences. The exposure created for the attendees of these events as well as their family and friends will likely be stronger than the media connections.

In conclusion, our results support the existence of the mere exposure effect in increasing familiarity and ultimately the decision to visit a city for business or pleasure.

Funding Acknowledgment

This research was supported by the 2022 Guangzhou Philosophy and Social Sciences Development Program during the 14th Five-Year Plan Period (Grant No. 2022GZGJ246).

This paper’s data, sourced from the 2023 Asian Youth Survey Project (AYSP) under ANPOR with contributions from China, India, Indonesia, Korea, Malaysia, Philippines, and Thailand, provides insights on perceptions of cities in Asia among students.