Urban Tourism Appeal: The Effectiveness of Communication Channels and Multimedia Formats in Creating Tourists’ Expectations

Abstract

Tourists’ expectations related to urban tourist destinations, which often derive from wider concept of destinations’ images, encompass a wide range of needs, desires and anticipations that tourists develop prior to visiting a particular destination. They are formed under the influence of different factors, one of which is traditional and digital communication channels. The objective of this research is to determine whether different communication channels and multimedia formats, used by tourists in the pre-travelling phase, could shape their expectations related to tangible and intangible characteristics of urban tourist destinations. The data gathering was conducted by applying Computer-Aided Web Interviewing (CAWI) on a representative sample of 312 adult citizens residing in Belgrade, the capital of the Republic of Serbia. The findings indicate that both traditional and digital communication channels, alongside personal recommendations used prior to the trip, significantly contribute to shaping respondents’ expectations concerning tangible characteristics of urban tourist destinations. Digital communication channels and personal recommendations appeared to be more important in shaping expectations related to the intangible characteristics of urban tourist destinations. Also, different formats of online media content exhibited positive associations with expectations concerning the tangible and intangible characteristics of urban tourist destinations. The obtained results provide recommendations for tourist organizations and local government entities, valuable for developing effective communication strategies for targeting potential tourists.

1. Introduction

The modern tourist market has undergone significant changes in recent years, driven by technological advancements, shifting consumer preferences, globalization, and various socio-economic factors. In accordance with that, urban tourist destinations are facing rapid changes in the global tourist market, which requires them to find the most efficient ways to respond to contemporary challenges and increasing competition. In this sense, the creation and management of a favourable image represents one of the most important challenges [1,2], taking into account that image is the basis for forming tourists’ expectations in relation to urban tourist destinations [3], which, consequently, lead to the creation of behavioural intentions. In this way, a well-created image would influence tourists’ intentions [4] and lead to an increase in the number of tourists who visit the destination [5] and use destination services [6]. Additionally, the importance of the destination image is also reflected in its potential to influence the sustainable development of tourism and tourist destinations [7,8].

Of particular importance for destination image creation and management is the effective use of communication through marketing and public relations. The marketing of urban tourist destinations has a crucial role in the process of creating and managing the destinations‘ images [9,10,11], based on emphasising and promoting tangible and psychological destination attractions [12]. On the other hand, the role of public relations is very important in the process of image management primarily through media communications, which is of particular importance when one takes into account that tourist destinations are very dependent on media publicity and the creation of media images [13,14]. In practice, marketing and media communications are carried out simultaneously and integratedly, through many different communication channels, through which valuable information is delivered for users of tourist services.

The global and highly competitive tourism market has forced tourist destinations to apply innovative ways of attracting tourists, primarily by using modern information and communication technologies [15], which is why traditional media are increasingly being replaced by digital ones [16]. Therefore, nowadays, social media represent an important communication channel in tourism [17]. Their significance is reflected in higher efficiency to obtain various information which further influences the perception of the destination and the decision to visit, compared to traditional media [18]. However, the role of traditional media should not be neglected in the contemporary environment as they are still perceived as quality information sources used by tourists of different demographic profiles.

Observing the above, it is clear that effective communication through various channels represents one of the most significant activities in managing urban tourist destinations image and, consequently, expectations related to them. In the realm of expectation research, the existing literature has primarily concentrated on assessing its influence on tourist satisfaction levels or the intention to visit a destination. However, there has been a notable absence of emphasis on understanding the origins of these expectations, particularly within emerging economies—an area that remains underexplored and distinct from studies in developed countries [19]. Additionally, even though the significance of conveying valuable information through different chanels in the phase before making the decision to visit a particular destination is explored and confirmed in the literature, the impact of different communication channels on building tourists’ expectations related to various destination characteristics is still underresearched. Such research is especially lacking in the context of tourists from developing countries, considering that they usually face resource limitations related to travelling to globally established urban destinations, and therefore, their expectations might differ from those of tourists coming from developed economies. In accordance with that, in order to address this research gap, the objective of the research presented in this paper is to determine whether different communication channels and multimedia formats, used by tourists in the pre-travelling phase, could shape their expectations related to the tangible and intangible characteristics of urban tourist destinations.

This paper is structured in the following manner: After the introductory part, the literature review related to destination image, tourists’ expectations, and communication channels is presented in Section 2. The research methodology is explained in Section 3, followed by the presentation of research results (Section 4) and discussion (Section 5). Finally, the authors’ concluding remarks, as well as future research directions and research limitations, are provided in the last section of the paper.

2. Literature Review

2.1. The Image of an Urban Tourist Destination

Destination image represents the sum of the beliefs, ideas, and impressions that an individual has about a destination [20,21,22,23]. The image of the destination in tourists’ minds is formed on the basis of cognitive (beliefs and knowledge), affective (feelings and emotions), and conative (intentions and behaviors) dimensions [21,22,23,24,25,26]. Generally observed, the image represents a kind of perception by the external public [27], which is why Papadimitriou et al. [28] view destination images as holistic images in the minds of potential tourists that determine their opinions and impressions about it. Chen et al. [29] stated that the tourism destination image represents the way the destination is observed from the tourists’ perspective. The significance of the destination image is reflected in its influence on the overall impression of the target public about the destination [30], leading to the ultimate goal of visiting the destination [28]. Therefore, tourists’ intentions to visit a certain city arise as a result of their perception and knowledge about it [31], where the image of the destination plays a significant role in that process [24,25].

Based on the model of travel experiences, proposed by Gunn [32], three types of destination image formation can be identified. An organic destination image is formed naturally based on the direct encounters and interactions that individuals have with the destination and word-of-mouth communication. An induced destination image refers to the image that is deliberately created and promoted by destination marketers, tourism boards, and other stakeholders through advertising, promotional campaigns, and other marketing efforts. The third type, called the composed image, represents a blend of both organic and induced elements, since it is formed through a combination of personal experiences and external influences such as marketing and media communications.

Attractions are key elements in creating the image of an urban tourist destination [9], which represent the unique characteristics and reflection of the identity of the destination [33,34]. Those characteristics should primarily provide a competitive advantage over other destinations [30] and enable differentiation in the global market. Navarro [35] suggested a classification of such characteristics, taking into account different types of criteria such as materiality, which can be tangible or intangible. With the example of cultural heritage, Ruggles and Silverman [36] explained the difference between tangible and intangible, so that the first refers to people rather than objects, while the focus of the second is on its tangible, monumental form. Rodzi et al. [37] stated that tangible heritage refers to something that is permanently being seen and touchable, such as a historical site, monument, building, old town centre, and palace. The authors explained intangible heritage through examples such as the following: performing arts, social practices, rituals, oral traditions, etc. Based on numerous works of research, Apostolopoulou and Papadimitriou [2] indicated that simply advertising the functional advantages of the destination does not have the desired effect, which is why the creation of the image of an urban tourist destination should rather be based on its symbolic and emotional characteristics, recognized by tourists.

2.2. Tourists’ Expectations Formed on the Basis of Urban Tourist Destination Image

Within the tourism sector, when contemplating a visit to a certain destination, people naturally form expectations regarding their future experience, including aspects such as accommodation, local transportation, and the quality of attractions they will encounter [38]. Tourists’ expectations related to tourist destinations encompass a wide range of desires, needs, and anticipations that travelers have prior to visiting a particular place. Such expectations, which tourists form in relation to a certain destination or attraction, influence their visiting intentions [39], experience, and satisfaction during all stages of the travel process [40]. Wisnawa et al. [41] explained tourists’ expectations as their hope that their needs would be met and satisfied. These expectations can be influenced by various factors such as personal preferences, previous experiences, cultural background, marketing efforts, and word-of-mouth recommendations.

It is highlighted in the literature that the destination image tends to be the source of tourist expectations [19]. Numerous authors emphasized the importance of image for the formation of tourists’ expectations regarding tourist destinations [8,20,24,42,43,44,45]. Pavković et al. [3] emphasized that expectations are formed in relation to all dimensions of the tourist destination’s attractiveness. Tourists’ experience with a destination is significantly dependent on their initial level of expectations [46]. In this sense, Jiang et al. [10] believe that the image of the destination must form expectations that can be fulfilled, because otherwise, it would lead to a sense of dissatisfaction among tourists.

As urban tourist destinations represent a complex entity, it is of particular importance to pay attention to what different tourists consider important [47] and expect from visiting them in order to be able to satisfy tourists by meeting their expectations, and, consequently enhance the attractiveness of a destination [48]. By taking into account that tourists’ expectations related to urban tourist destinations can change over time, it is necessary to constantly adapt the image of the city to those changes and continuously improve it [1].

2.3. The Role of Communication Channels and Multimedia Formats in Creating Tourists’ Expectations

This research has its foundations in the Uses and Gratifications Theory, developed by Katz et al. [49], which focuses on the active role of media audiences in selecting and using media to satisfy their needs and desires. Unlike traditional mass communication theories that view audiences as passive recipients of media messages, the Uses and Gratifications Theory emphasizes that individuals are motivated to seek out specific media content to fulfill psychological and social needs. This theory has been applied in various aspects of tourism studies, including the influence of gratification provided by various media on tourists’ attitudes and intentions to visit tourist attractions [50,51,52] and hotels [53], individual and social travelling motivations [54], satisfaction with travel experience [55], etc. This study employed the Uses and Gratifications Theory to explore the relationship between significance attached to different communication channels and media formats by tourists prior to their trips, and expectations related to the attractions offered by the urban destination.

Previous research emphasizes the importance of marketing communications for tourism [56,57,58], which are considered a significant contribution to building the images of destinations and tourists’ behavioural intentions in relation to them [4]. Marketing communications, according to Szromnik [59], represent one of the most important marketing approaches of organizations at urban destinations in their efforts to influence the target public in tourism, primarily through the transmission of information about their characteristics and attributes important for potential tourists. Precisely for this reason, Štefko et al. [60] point out that the strategic decision of choosing the most effective communication channels is of particular importance.

There is an extensive body of literature emphasizing the significance of communications in the process of forming and managing destination image. In the context of image creation, Leiper [61], introduced the term „detached markers“ that refers to information that tourists receive spatially separate from attractions, as well as „contiguous markers“ that imply information obtained at the location itself. Detached markers can be further divided into generating markers and transit markers. The first term implies information that tourists receive before the trip, while the other refers to the information tourists receive during the trip to the destination. This approach is based on the fact that tourist services are bought in advance and at time and spatial distance from the moment and place of consumption, so tourists must rely on descriptions of destinations and their offers [45,62]. Very similarly, Hysa et al. [63] stated that extensive use of the information that tourists receive before, during, and after the trip is crucial for destination brand building, emphasizing the role of social media in each of these different stages of the journey. In this sense, Michaelidou et al. [30] believed that, since the image is often crucial in the process of considering and deciding on the destinations tourists want to visit, timely and accurate information, relevant to tourists’ needs and desires, is crucial for gaining destinations‘ competitiveness. In this process, various media channels are crucial for creating the destination’s image and expectations related to it [44,64] and represent a critical information channel for tourists in choosing a destination to visit [65], based on what they have heard, read, and seen through these communication channels [66].

Although traditional media, such as television, radio, print, and out-of-home media, represent a very important source of information for tourists [11], the role of internet-based channels, especially social media, in creating a destination’s image is increasingly dominant [24,67,68]. The importance of social media in tourism has become unquestionable [69,70,71], given that they represent a significant source of information on the basis of which tourists form a perception of a certain destination [33] and consequently intentions and decisions to visit [72,73,74]. The main advantages of communication and image building in tourism through social media are providing the public with the opportunity to search for information, create their own content and share it with others [67], as well as to follow and rate content from other sources [70]. Dealing with the importance and role of social media, numerous authors have contributed to this topic, based on which it is possible to identify the most important social media in tourism and tourist destinations: Instagram [17,75], Facebook [67,71], X (formerly Twitter) [17,75], YouTube [75], TikTok [76,77], blogs and travel blogs [67,73,78], TripAdvisor [17,70,71,79], and Airbnb and Booking.com [71]. Huang et al. [80] stated that Instagram is a valuable channel for increasing the visibility of tourist attractions, since the frequency of a destination being captured on Instagram influences tourists’ perception of the destination. Giving an example of an urban festival, Kádár and Klaniczay [81] found out that Instagram photos of tourists’ experiences related to a certain event contributed to the branding process, not only of the event, but also the destination and less known heritage values. Jadhav et al. [82] noted that Facebook exerts significant influence on every stage of travel planning, serving as the initial touchpoint even when individuals are in a passive state, and continuing to play a role in post-travel advocacy. Coronel Padilla et al. [83] stated that Facebook represents a powerful tool to project a city’s image and make an impact on the expectations, motivations, and behaviors of visitors and potential visitors, especially when cities promote local culture and identity through this communication channel. Huertas et al. [84] emphasized that Twitter is a very effective communication channel for creating interactions with stakeholders and for branding cities.Top of Form

Shin et al. [85] conducted research related to the effectiveness of YouTube channels on the perception of a destination by tourists. The results showed the effectiveness of video content for the perception of the destination by tourists, especially if tourists recognize it as non-advertising content. Li et al.’s [76] research results indicated that short video formats on Tik Tok had an impact on the formation of the cognitive, affective, and conative image of a destination. Gao [77] noted that Tik Tok has brought new opportunities for the construction and dissemination of urban images, enabling small and medium-sized cities to attract tourists’ attention. Jang and Park [86] emphasized that sharing travel experiences through blogs has a significant impact on the formation of expectations among potential tourists and the promotion of a certain destination. Mkono and Tribe [70] consider TripAdvisor one of the most important social media in tourism, given that it enables interaction with stakeholders, both through reviews and discussion forums (sharing opinions, tips, and experiences in interactive discussions with the community).

The intention to visit a certain destination, according to Abubakar and Ilkan [78], arises as a result of receiving information from different sources, including eWOM, which is perceived as a far more reliable source of information about the destination than official channels, by potential tourists. Thus, the importance of user-generated content as information source in tourism is especially emphasized in the literature, enabling tourists to act as both users and creators of online content related to sharing experiences before, during, and after the trip [87]. Widayati et al. [88] emphasized the relationship between destination image and visiting decisions made on the basis of WOM communication. The significance of UGC in creating expectations about tourist destinations is based on the trust which tourists feel regarding such content, considering it is created by other tourists and not induced by destination bodies [89]. Chen [90] stated that social media have become a vital platform for users to share their thoughts, opinions, and perceptions. In the realm of sustainable city communication, public engagement on social media, through actions such as liking, sharing, or commenting, plays a pivotal role. These interactions influence public trust, shape perceptions of the city, and ultimately enhance its overall image.

Based on the results of previous research, it is reasonable to assume that communication channels shape the expectations of tourists regarding different characteristics of urban tourist destinations. In accordance with that, the following research questions can be drawn:

RQ1: Do different types of communication channels influence expectations regarding the tangible characteristics of urban tourist destinations?

RQ2: Do different types of communication channels influence expectations regarding the intangible characteristics of urban tourist destinations?

Some authors (e.g., Nóbrega et al. [91], Pavković et al. [92], and Leung et al. [72]) particularly emphasized the importance of multimedia content in the process of transmitting desired information to target audiences. Szromnik [59] believes that multimedia has a crucial role in creating the image of the city as a tourist destination and its superiority over competitors. In this sense, by observing the importance of different multimedia formats in the online world, authors emphasized the importance of text [75], photographs [17,75,93], video content [75,93], and virtual reality [15,94,95]. Xiao et al. [96] found that analysis of the visual content of tourist photos represents an effective manner to explore a tourist destination’s image. Hysa et al. [63] particularly emphasized the importance of creating a city’s recognition through photos on social media such as Instagram. The results of Shin et al. [85] and Li et al. [76] showed the effectiveness of video content for the perception of a destination’s image by tourists. The results of Griffin et al.’s study [97] showed the significance of active VR content for the effectiveness of destination marketing strategies.

In accordance with that, two additional reserach questions are posed:

RQ3: Do different multimedia formats influence expectations regarding the tangible characteristics of urban tourist destinations?

RQ4: Do different multimedia formats influence expectations regarding the intangible characteristics of urban tourist destinations?

The conceptual framework of the study, illustrating the relation between variables, is presented in Figure 1.

3. Methodology

This research was conducted by using a questionnaire-based survey in 2020. The data gathering approach utilized Computer-Aided Web Interviewing (CAWI) to conduct the survey on a representative sample of adult citizens residing in Belgrade, the capital of the Republic of Serbia, by using a web-based form. This research employed a representative, random stratified sampling method. Trained researchers administered the questionnaire, ensuring that each respondent was briefed on the research purpose and objectives. After designing the questionnaire, it was uploaded to a web server where it could be accessed by respondents who were invited through email invitations. The selection of email addresses was randomized. Participants accessed the survey using a web browser on their computer or smartphone and answered the questions by selecting options or typing in responses directly into the online form. As respondents completed the survey, their answers were automatically collected and stored in a database and were further analyzed. After the collection of data was completed, data analysis focused on only 312 respondents who fulfilled explicit criteria necessitating visits to at least two cities outside Serbia within the past five years. At the outset of the survey, several screening questions were employed to filter respondents. Firstly, individuals who had not visited at least two foreign cities within the past five years were deemed unsuitable for the study. The study targeted individuals with recent travel experience, presuming that their responses would be more reliable since their memories and impressions of visited places remained fresh. Secondly, the study sought respondents who had spent several days at tourist destinations; therefore, individuals who had only visited cities in passing or during brief one-day excursions while travelling to their final destination were excluded from the study. Lastly, tourism professionals were omitted from the sample as their attention to nuanced details not pertinent to average tourists was believed to potentially skew and distort the research results.

The analysis of data was conducted utilizing SPSS 29 software. Besides descriptive statistics, partial correlations and hierarchical multiple regression were employed to explore the relationships between the importance attached to different communication channels and multimedia formats by respondents and their expectations regarding specific tangible and intangible characteristics of destinations they have visited, controlling for the effect of demographic characteristics.

3.1. Questionnaire

The questionnaire consisted of three segments. At the beginning, participants were instructed to select one urban tourist destination they had visited. Subsequently, they assessed their expectations prior to their visit by rating each specific characteristic of the destination, divided into tangible (CHA, FE, HEF, SF, CC, PA, PGA, AF, CT, and TW) and intangible (SAF, INT, AUT, and PAM) (

Table 1. Respondents’ expectations related to tangible and intangible characteristics of tourist destinations.

Tangible Characteristics of Tourist Destinations		Labels
Expectations related to primary tourists attractions *	Quality of cultural and historical attractions	CHA
	Quality of festivals and events	FE
	Quality of hospitality and entertainment and leisure facilities	HEF
	Quality of shopping facilities	SF
Expectations related to secondary tourist attractions *	Cleanliness of the city	CC
	Price affordability	PA
	Arranged parks and green areas	PGA
	Quality of accommodation facilities	AF
	Convenient transportation and easy access to all tourist facilities in the city	CT
	Quality of tourist facilities along the waterfront	TW
Intangible characteristics of tourist destinations
Safe		SAF
Interesting		INT
Authentic		AUT
Pleasant ambience		PAM

* Note: Obtained by applying factor analysis.

). The metrics employed in this research align with the delineation of tourist destination attractiveness dimensions as outlined by Pavković et al. [3]. All inquiries followed the format of “Indicate your level of agreement with the following statements:” with each statement relating to expectations regarding distinct characteristics of a tourist destination prior to their visit to the destination nominated by each respondent. The assessment was conducted on a four-point Likert scale ranging from 1—“strongly disagree” to 4—“strongly agree”. Respondents were provided with an option outside the scale, granting them the opportunity to indicate that they had no expectations regarding certain dimensions of tourist destination attractiveness (as recommended by Kulas et al. [98]). Many researchers in the field of tourism opt for an even-numbered scale to mitigate the risk of respondents selecting the midpoint even when their true opinion is not neutral (e.g., Chrobak et al. [99]; Lin et al. [100]; Lopez-Sanz et al. [101]).

In the second segment of the questionnaire, respondents rated the extent to which different communication channels influenced their expectations regarding the urban tourist destination they have visited (

Table 2. Communication channels used for collecting tourist information.

	Communication Channels	Labels
Traditional communication channels *	Television	TV
	Out-of-home media	OOH
	Employees in travel agencies	ETA
	Tourism fairs	TF
	Radio	RD
	Print media	PM
	Printed promotional materials	PP
Digital communication channels and personal recommendations *	Online reservation systems (Booking, Airbnb)	OLRS
	TripAdvisor	TA
	Online communication content created by other users	UGC
	Google Maps	GM
	Word of mouth and personal recommendation	WOM
	Official online channels of communication of the tourism organization of the city or region, national tourism organization, competent ministry, city, or hotel	OOLC

* Note: Obtained by applying factor analysis.

). The ratings were given on a four-point Likert scale, ranging from 1—“it had no influence at all” to 4—“it influenced to a great extent” with the possibility to choose “Haven’t used this channel at all”. The categorization of communication channels into traditional (offline) and digital (online) is a common framework used in various fields, including communication studies, marketing, and media research. Shimp and Andrews [102] made a distinction between traditional (television, print, radio) and digital (online and mobile) advertising media. Clow and Baak [103] mention traditional media channels (television, print, radio, OOH) and digital and mobile marketing channels. The metrics employed in this research align with this general systematization of media into traditional and digital, focusing on those which have the greatest implication in tourism [17,67,70,71,75,94,95].

In addition to communication channels, respondents expressed the extent to which different online multimedia formats influenced their expectations regarding the urban tourist destination prior to their visit on a four-point Likert scale. These formats were as follows: Video (online video content posted by individuals who have visited the destination, local and national tourist organizations, accommodation facilities and travel agencies), Photographs (online photos posted by individuals who visited the destination, local and national tourist organizations, accommodation facilities and travel agencies), Text (online texts and comments posted by individuals who visited the destination, local and national tourist organizations, accommodation facilities, and travel agencies) and Virtual Reality (a panoramic view of 360°).

The questionnaire is presented in Appendix A.

3.2. Sample

The representative sample included 312 respondents who reside in Belgrade. The male population was represented by 46.3%, while the female population was represented by 53.7% of the total sample. In relation to age, the respondents were divided into four groups. Respondents belonging to Generation Z (18 to 23 years) represented 27.3% of the sample, Generation Y (24 to 39 years) 29.5%, Generation X (40 to 55 years) 31.5%, and the Baby Boom generation (56 to 74 years) 11.7%. In relation to the level of income, the largest number of respondents were in the group of average income (56.3%), followed by the group of below average income (21.4%) and above average (22.3%). The highest number of respondents were holders of a master’s degree (30.8%), followed by bachelor’s degree (25.6%), college degree (19.6%), high school (18.6%), and doctoral degree (5.4%).

4. Results

4.1. Descriptive Statistics

In the context of expressing the expectations regarding both the tangible and intangible characteristics of urban destinations they had before visiting those destinations, respondents appointed relatively high values (mean values above 3) to all items. The highest expectations in the case of the tangible characteristics were regarding CHA, CT, and PA. Considering expectations related to the intangible characteristics of tourist destinations, the mean scores were approximately the same for all four items in question. The analysis related to those characteristics showed a relatevely small standard deviation, indicating the coherence of respondents’ answers.

Respondents rated WOM (3.46 ± 0.753), UGC (3.34 ± 0.875), and OOLC (3.19 ± 0.820) as the most important channels for gathering tourism-related information, while the least importance was attributed to RD (1.95 ± 1.052) and ODM (1.99 ± 1.075), both with a high standard deviation. It is evident that the respondents attached more importance to online channels of communication compared to offline channels, which was expected in the contemporary context of the tourist market (

Table 3. Mean values of participants’ evaluations on a four-point Likert scale.

Communication Channels	Mean Values	Std. Dev.
TV	2.65	1.027
OOH	1.99	1.075
ETA	2.23	1.110
TF	2.21	1.149
RD	1.95	1.052
PM.	2.45	0.971
PP	2.30	1.038
OLRS	3.11	1.012
TA	2.99	1.087
UGC	3.34	0.875
GM	3.16	1.019
WOM	3.46	0.753
OOLC	3.19	0.820
Multimedia formats
Video	3.30	0.822
Photographs	3.43	0.677
Text	3.36	0.758
VR	2.60	0.995

).

Regarding multimedia formats, mean scores indicated slightly greater importance attached to Photographs than to Text and Video. The least importance was attributed to VR as a means of conveying important information in the pre-travel phase.

4.2. Factor Analysis

In order to reduce the number of variables, factor analysis was applied as a first step in the analysis. By applying factor analysis, respondents’ expectations related to tangible characteristics of urban tourist destinations was divided into two factors. The first factor was labeled expectations related to primary tourists attractions (explaininig 39.605% of variance) (EPTA) consisted of the following: CHA, FE, HEF, and SF. The second factor, entitled expectations related to secondary tourist attractions (explaining 15.896% of variance) (ESTA) consisted of the following elements: CC, PA, PGA, AF, CT, and TW. This is in accordance with the classification of Leiper [46] who divided tourist attractions into three groups. The characteristics familiar to tourists prior to their visit, which affected their decision, were classified as primary attractions (as reflecting the essence of the destination), whereas those characteristics also known to tourists before their visit but without the power to influence their decision were classified as secondary. The third group consisted of those characteristics which tourists were not familiar with before the trip; therefore, it was not the subject of this research. The two obtained factors were subjected seperately to further analysis. Due to its specificity and the lower number of intangible characteristics of tourist destinations, factor analysis was not carried out for expectations related to the intangible characteristics of urban destinations, so they were, accordingly, observed individually in the further analysis.

Aiming at analyzing the relationship between pre-travel communication channels and expectations regarding the tangible and intangible characteristics of urban tourist destinations in the most efficient way, a factor analysis of communication channels was conducted as well. By factor analysis, a larger number of communication channels, as source variables, were grouped into two factors. The first factor (explaining 52.203% of variance) included the following communication channels: TV, OOH, ETA, TF, RD, PP, and PM; so, based on their nature they were labeled traditional communication channels (TCCs). The second factor (explaining 19.664% of variance) consisted of the following communication channels: OLRS, TA, UGC, GM, WOM, and OOLC. Based on the character of these channels, they were named digital communication channels and personal recommendations (DCCs). After conducting a factor analysis related to pre-travel communication channels, these two factors were used as subjects of further analysis. The four multimedia formats taken into consideration were analyzed separately in relation to other variables.

4.3. Examinining the Relationship between Communication Channels and Expectations Regarding Tangible Characteristics

In order to examine whether there was a relationship between communication channels and expectations regarding the tangible and intangible characteristics of urban tourist destinations, correlation analyis was applied. First, correlation analysis was applied to determine the nature of the relationship between TCCs and DCCs as independent variables, and EPTA and ESTA as dependent variables. The obtained correlation coefficients indicate that there was a positive association between both TCCs and DCCs and both types of expectations regarding tangible characteristics—EPTA and ESTA, meaning that, controlling for the effect of demographic characteristics, by increasing the value of both groups of communication channels before the trip, the values of both types of expectations were also increasing in relation to the tangible characteristics. The greatest correlation was identified between DCCs and EPTA (0.492), followed by the correlation between TCCs and EPTA (0.379). The correlation between TCCs and ESTA was 0.376, while the correlation between DCC and ESTA was the lowest (0.224).

Additionally, hierarchical multiple regression was applied to examine the association of traditional and digital communication channels with EPTA, controlling for the influence of demographic variables such as gender, age, education level, and the level of income (

Table 4. The influence of communication channels on respondents’ expectations related to primary tourist attractions (EPTA).

Model 2 a	Unstandardized Coefficients		Standardized Coefficients	t-Value	Sign.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	2.093	0.215		9.757	0.000
Gender	−0.276	0.061	−0.214	−4.557	0.000	0.949	1.054
Income	−0.022	0.026	−0.045	−0.854	0.394	0.763	1.310
Education	0.100	0.029	0.188	3.515	0.001	0.733	1.364
Age	−0.001	0.003	−0.014	−0.296	0.767	0.880	1.136
TCC	0.145	0.042	0.198	3.477	0.001	0.647	1.546
DCC	0.335	0.046	0.391	7.229	0.000	0.713	1.403

^a Dependent Variable: EPTA; adjusted R² = 37%.

). To examine the presence of multicollinearity, tolerance and VIF values were also calculated. Tolerance values above 0.10 and VIF values below 10, or 5 as a more conservative threshold [104], indicate that the variables are not highly correlated and that multicollinearity is not a critical issue of the present model.

Demographic variables were entered into the regression equation in the first block of variables, whereas TCCs and DCCs were entered into the second block of independent variables, using ‘enter’ as a method of entry of independent variables. Model 2 was significant (F(6, 296) = 30.507, p < 0.001), indicating that the regression model fitted the data well and resulted in an explained variance in EPTA of 37%. The change of R² between Model 1 and Model 2 was statistically significant (R²Change = 0.231, p < 0.001), indicating unique variance in EPTA that is accounted for by TCCs and DCCs, above and beyond variance in the dependent variable that has already been accounted for by demographic variables. Therefore, the inclusion of TCCs and DCCs into the regression model contributed to the increase of explained variance in EPTA to a total of 37%. As the demographic variables solely explained 14% of variance in expectations and the inclusion of TCCs and DCCs contributed to the total explained variance of 37%, this change indicates that the inclusion of TCCs and DCCs represents a more powerful set of determinants of expectations than the set of demographic characteristics solely. Both TCCs (β = 0.198, p < 0.05) and DCCs (β = 0.391, p < 0.001) emerged as statistically significant and positive determinants of EPTA; however, the impact of DCCs was stronger. This finding implies that a higher reliance of prospective visitors on information conveyed via digital communication channels and word-of-mouth recommendations leads to higher expectations in comparison with those which would be shaped by traditional communication channels solely. Gender, a variable with only two categories, emerged as a significant but negative determinant of EPTA (B = −0.276, β = −0.214, p < 0.001), meaning that moving from the base category (male) to the other (female) decreases expectations by 0.276 units; i.e., female visitors have lower expectations than males. An increase in education level by one unit leads to the rise in expectations related to primary tourist attractions by 0.100 units; i.e., more educated visitors have higher expectations related to primary tourist attractions.

Hierarchical multiple regression was used to assess the impact of traditional and digital communication channels on expectations related to secondary tourist attractions after controlling for the impact of demographic variables (

Table 5. The influence of communication channels on respondents’ expectations related to secondary tourist attractions (ESTA).

Model 2 a	Unstandardized Coefficients		Standardized Coefficients	t-Value	Sign.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	2.813	0.166		16.985	0.000
Gender	0.073	0.047	0.083	1.554	0.121	0.947	1.056
Income	−0.004	0.020	−0.012	−0.206	0.837	0.766	1.306
Education	0.063	0.022	0.173	2.859	0.005	0.732	1.367
Age	−0.005	0.002	−0.133	−2.422	0.016	0.882	1.134
TCC	0.184	0.032	0.366	5.681	0.000	0.643	1.554
DCC	0.037	0.036	0.064	1.032	0.303	0.705	1.419

^a Dependent Variable: ESTA; adjusted R² = 19.8%.

). All tolerance and VIF values were acceptable, implying no excess multicollinearity in the model. Model 2, after the inclusion of TCCs and DCCs, was significant (F(6, 294) = 13.353, p < 0.001) and resulted in an explained variance in ESTA of 19.8%. The inclusion of TCCs resulted in significant increase of explained variance (R²Change = 0.132, p < 0.001). Model 2 resulted in the significant impact of TCCs on ESTA (β = 0.366, p < 0.001), whereas the contribution of DCCs toward ESTA was not statistically significant, implying that it does not contribute any additional explanatory power to the equation beyond the impact of TCCs. This finding implies that a higher reliance of potential tourists on traditional communication channels in obtaining information about a destination leads to a higher level of expectations related to secondary attractions. A possible explanation of this finding is that the majority of respondents who participated in the study are users of tourist agencies’ services, who have visited foreign urban destinations as members of group package tours. In that case, they primarily rely on information obtained from tourist agencies’ personnel regarding price affordability, quality of accommodation, and transportation options in a destination, whereas those who organize trips by themselves are more likely to seek information via Booking, TripAdvisor, and the digital communication channels of a tourist organization in a destination. This remark merits further investigation. Education level emerged as a significant and positive determinant of ESTA (B = 0.063, β = 0.173, p < 0.05), meaning that an increase in education level by one unit increases visitors’ expectations related to secondary tourist attractions by 0.063 units. Age emerged as a significant, but negative predictor of expectations (B = −0.005, β = −0.133, p < 0.05), implying that older visitors have slightly lower expectations related to secondary tourist attractions.

4.4. Exploring the Relationship between Communication Channels and Expectations Regarding Intangible Characteristics

By applying partial correlation, the relationship between the TCCs and DCCs, as independent variables, and expectations related to intangible characteristics, as dependent variables, was examined. The correlation analysis results revealed a positive correlation between DCCs and expectations regarding all four intangible characteristics taken into consideration. The highest correlation was determined between DCCs and AUT (0.342) and DCCs and PAM (0.338). The correlation between DCCs and SAF was 0.337, whereas the correlation was the lowest in case of DCCs and INT (0.303). Hence, elevating the significance of communication channels by respondents led to an increase in the values of all intangible characteristics of urban destinations. Conversely, TCCs were not correlated with the intangible characteristics of destinations.

In the continuation of the research, hierarchical multiple regression was used to analyze if DCCs and TCCs significantly influenced intangible characteristics (

Table 6. The influence of communication channels on respondents’ expectations related to intangible characteristics.

Model 2	Unstandardized Coefficients		Standardized Coefficients	t-Value	Sign.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	2.408	0.227		10.627	0.000
Gender	0.008	0.064	0.007	0.130	0.896	0.941	1.062
Income	0.003	0.027	0.006	0.095	0.925	0.756	1.323
Education	0.086	0.030	0.177	2.830	0.005	0.715	1.399
Age	0.001	0.003	0.012	0.217	0.829	0.870	1.149
TCCs	−0.052	0.044	−0.079	−1.186	0.237	0.638	1.568
DCCs	0.294	0.049	0.375	5.967	0.000	0.709	1.411
Dependent variable: Safe (SAF); adjusted R2 = 17.3%
(Constant)	3.004	0.188		15.942	0.000
Gender	0.053	0.052	0.057	1.020	0.309	0.944	1.059
Income	−0.014	0.022	−0.040	−0.638	0.524	0.750	1.333
Education	0.035	0.025	0.091	1.412	0.159	0.713	1.403
Age	0.001	0.002	0.026	0.453	0.651	0.869	1.150
TCCs	−0.086	0.036	−0.162	−2.399	0.017	0.646	1.547
DCCs	0.244	0.041	0.382	5.967	0.000	0.719	1.391
Dependent variable: Interesting (INT); adjusted R2 = 12.8%
(Constant)	2.858	0.188		15.188	0.000
Gender	0.018	0.052	0.019	0.338	0.735	0.943	1.061
Income	−0.008	0.022	−0.022	−0.361	0.719	0.761	1.315
Education	0.048	0.025	0.123	1.922	0.056	0.711	1.407
Age	0.002	0.002	0.047	0.817	0.415	0.873	1.146
TCCs	−0.045	0.036	−0.083	−1.240	0.216	0.642	1.558
DCCs	0.249	0.041	0.391	6.091	0.000	0.701	1.426
Dependent variable: Authentic (AUT); adjusted R2 = 15.2%
(Constant)	2.894	0.181		15.993	0.000
Gender	0.056	0.050	0.063	1.127	0.261	0.945	1.059
Income	0.006	0.021	0.017	0.269	0.788	0.762	1.313
Education	0.025	0.024	0.068	1.063	0.289	0.721	1.386
Age	0.001	0.002	0.024	0.405	0.686	0.869	1.151
TCCs	−0.045	0.034	−0.089	−1.319	0.188	0.653	1.530
DCCs	0.235	0.039	0.389	6.071	0.000	0.725	1.379
Dependent variable: Pleasant Ambience (PAM); adjusted R2 = 13.2%

).

In the examination of the contribution of traditional and digital communication channels, over and above the impact of demographic variables, to visitors’ expectations related to SAF, Model 2 was statistically significant (F(6, 289) = 11.306, p < 0.001) and resulted in an explained variance in expectations related to SAF of 17.3%. The change of R² between the first and the second model was statistically significant (R²Change = 0.108, p < 0.001), Whereas the impact of traditional communication channels was not statistically significant, digital communication channels emerged as a significant determinant of SAF (β = 0.375, p < 0.001). This finding implies a potential for destination management organizations and tour operators to rely on personal recommendations of previous visitors and their e-WOM in assuring prospective visitors of the safety of a destination. In addition, education level also emerged as a statistically significant and positive determinant of SAF (B = 0.086, β = 0.177, p < 0.05), implying slightly higher expectations related to the safety of a destination among more educated visitors.

Hierarchical multiple regression, which was applied to examine the impact of traditional and digital communication channels on expectations related to an interesting offer of a destination, after controlling for the impact of demographic characteristics, resulted in significance in Model 2 (F(6, 290) = 8.228, p < 0.001) and an explained variance in this intangible characteristic of a destination of 12.8%. The change of R2 between Model 1 and Model 2 was statistically significant (R²Change = 0.105, p < 0.001). Whereas DCCs emerged as a statistically significant and positive determinant of INT (β = 0.382, p < 0.001), the impact of TCCs was statistically significant but negative (β = −0.162, p < 0.05) This finding implies that the more prospective visitors rely on traditional communication channels in informing themselves about a destination, the lower visitors’ expectations of a tourist destination as an interesting place to visit. Conversely, the higher reliance of prospective visitors on digital communication media content, the higher their expectations of an urban destination as an interesting place to visit. A plausible explanation for this finding lies in urban tourist destination organized tours. Tourists buy those arrangements most often before the very beginning of the trip. The demand for agencies’ services is at its peak at that time and agencies’ front-line personnel direct tourists to get additional information about the destination at their websites and other digital communication channels, remaining mainly at prospective clients’ disposal for information about the arrangement itself, including its duration and pricing. In those circumstances, it is reasonable to expect that digital communication channels would be more effective in contributing to tourists’ higher expectations of a destination as an interesting place to visit.

The application of hierarchical multiple regression, to examine the impact of communication channels on Authenticity, over and above demographic characteristics, resulted in significance in Model 2 (F(6, 287) = 9.741 p < 0.001) and an explained variance in Authenticity of 15.2%. The change of R² between Model 1, including only demographic characteristics, and Model 2, including communication channels in addition to demographic variables, was statistically significant (R²Change = 0.115, p < 0.001). Digital communication channels emerged as a positive and the only influential determinant of Authenticity (β = 0.391, p < 0.001). This finding reveals a potential for destination management organizations and tour operators to employ electronic media, personal recommendations, and e-WOM of previous visitors to convey the message of a destination as an authentic place to visit.

Hierarchical multiple regression, with the entrance of TCCs and DCCs in the second block of independent variables, and Pleasant Ambience as the dependent variable, resulted in a significant regression model (F(6, 285) = 8.396, p < 0.001) and an explained variance in ambience of 13.2%. The change of R² between Model 1, including only demographic characteristics, and Model 2, including communication channels in addition to demographic variables, was statistically significant (R²Change = 0.116, p < 0.001). Digital communication channels emerged as a positive and the only significant determinant of expectations of the pleasant ambience of a tourist destination (β = 0.389, p < 0.001). This implies that the increased reliance of prospective tourists on digital communication channels in informing themselves about a destination would lead to tourists’ increased expectations of the pleasantness of the ambience of a destination. The aforementioned finding reveals a potential for destination management organizations and tour operators to employ digital channels and personal recommendations to increase prospective tourists’ expectations related to the ambience of a destination and motivate a visitation.

4.5. The Relationship between Multimedia Formats and Expectations Regarding Tangible and Intangible Characteristics of Urban Tourist Destinations

Partial correlation was applied in order to examine the connection between multimedia formats as independent variables and both groups of expectations related to tangible characteristics as dependent variables. The results of the analysis showed that there was a positive correlation between all multimedia formats and both EPTA and ESTA. In this sense, the more the respondents perceived these multimedia formats to be of higher significance, the higher their expectations regarding primary and secondary tourist attractions would be. In the context of correlation with tangible characteristics, the greatest correlation occured between Video and EPTA (0.434) and Text and EPTA (0.422). Also, it was noticeable that all multimedia formats demonstrated greater connection with EPTA compared to ESTA. In addition, the results of the correlation analysis showed that there was a positive relationship between Video, Photographs, and Text and expectations regarding all intangible characteristics of urban tourist destinations. Also, a positive association was confirmed in the case of VR and SAF and PAM, but not in relation to INT and AUT. In this sense, the higher the perceived importance of multimedia formats, the higher the expectations regarding these intangible characteristics were. The highest association was discovered between Text and AUT (0.400), followed by Video and AUT (0.378) and Photographs and SAF (0.377).

Furthermore, a hierarchical multiple regression was used to test if multimedia formats significantly contributed to the explained variance in EPTA, over and beyond demographic characteristics. The overall regression was statistically significant (F(8, 271) = 18.825, p < 0.001). It was found that Video significantly predicted EPTA (β = 0.268, p < 0.05), as well as Text (β = 0.194, p < 0.05) and VR (β = 0.161, p < 0.05). On the other hand, Photographs did not significantly predict EPTA (

Table 7. The influence of multimedia formats on respondents’ expectations related to primary tourist attractions.

Model 2 a	Unstandardized Coefficients		Standardized Coefficients	t-Value	Sign.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	2.281	0.239		9.541	0.000
Gender	−0.247	0.063	−0.197	−3.943	0.000	0.950	1.053
Income	−0.014	0.026	−0.029	−0.549	0.584	0.835	1.198
Education	0.089	0.028	0.172	3.175	0.002	0.809	1.237
Age	−0.004	0.003	−0.072	−1.409	0.160	0.915	1.092
Video	0.204	0.068	0.268	3.011	0.003	0.300	3.331
Photographs	−0.049	0.082	−0.054	−0.598	0.550	0.295	3.385
Text	0.163	0.065	0.194	2.503	0.013	0.394	2.535
VR	0.101	0.034	0.161	2.933	0.004	0.791	1.264

^a Dependent Variable: EPTA; adjusted R² = 33.8%.

). The model explains 33.8% of variance in EPTA. Education also emerged as a significant predictor of EPTA (B = 0.089, β = 0.172, p < 0.05), implying that one unit increase in education leads to 0.089 units increase in expectations related to primary tourist attractions. In addition, Gender emerged as a significant, but negative predictor of EPTA (B = −0.247, β = −0.197, p < 0.001), indicating lower expectations of female tourists related to primary tourist attractions.

When testing whether media formats significantly predicted ESTA, over and beyond demographic characteristics, (F(8, 270) = 6.372, p < 0.001), VR emerged as the only significant predictor of ESTA (β = 0.148, p < 0.05) among multimedia formats. On the other hand, Video, Photographs, and Text did not significantly predict ESTA (

Table 8. The influence of multimedia formats on respondents’ expectations related to secondary tourist attractions.

Model 2 a	Unstandardized Coefficients		Standardized Coefficients	t-Value	Sign.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	2.887	0.193		14.924	0.000
Gender	0.063	0.051	0.071	1.234	0.218	0.948	1.055
Income	0.024	0.021	0.069	1.132	0.259	0.832	1.201
Education	0.016	0.023	0.042	0.683	0.495	0.805	1.242
Age	−0.008	0.002	−0.206	−3.533	0.000	0.915	1.093
Video	0.040	0.055	0.075	0.735	0.463	0.300	3.335
Photographs	0.081	0.066	0.125	1.216	0.225	0.294	3.401
Text	0.005	0.053	0.009	0.100	0.920	0.391	2.556
VR	0.066	0.028	0.148	2.360	0.019	0.792	1.263

^a Dependent Variable: ESTA; adjusted R² = 13.4%.

). The model explains 13.4% of variance in ESTA.

In the next step, the relationship between multimedia formats and expectations regarding the intangible characteristics of urban tourist destinations was tested by applying a hierarchical multiple regression (

Table 9. The influence of multimedia formats on respondents’ expectations related to intangible characteristics.

Model 2	Unstandardized Coefficients		Standardized Coefficients	t-Value	Sign.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	1.914	0.252		7.583	0.000
Gender	−0.019	0.065	−0.016	−0.287	0.774	0.947	1.056
Income	0.013	0.027	0.028	0.486	0.627	0.831	1.203
Education	0.094	0.029	0.193	3.225	0.001	0.799	1.252
Age	0.002	0.003	0.042	0.748	0.455	0.903	1.107
Video	−0.044	0.070	−0.060	−0.627	0.531	0.306	3.268
Photographs	0.255	0.085	0.291	3.009	0.003	0.306	3.272
Text	0.153	0.067	0.191	2.277	0.024	0.407	2.456
VR	−0.003	0.036	−0.004	−0.071	0.943	0.797	1.255
Dependent variable: Safe (SAF); adjusted R2 = 21.3%
(Constant)	2.894	0.207		13.978	0.000	2.894	0.207
Gender	0.029	0.052	0.033	0.566	0.572	0.029	0.052
Income	−0.030	0.022	−0.087	−1.392	0.165	−0.030	0.022
Education	0.058	0.023	0.157	2.484	0.014	0.058	0.023
Age	0.003	0.002	0.062	1.035	0.301	0.003	0.002
Video	0.106	0.055	0.188	1.913	0.057	0.106	0.055
Photographs	0.102	0.068	0.147	1.500	0.135	0.102	0.068
Text	0.031	0.054	0.050	0.576	0.565	0.031	0.054
VR	−0.042	0.028	−0.092	−1.458	0.146	−0.042	0.028
Dependent variable: Interesting (INT); adjusted R2 = 12.8%
(Constant)	2.648	0.205		12.937	0.000
Gender	0.016	0.052	0.017	0.317	0.752	0.945	1.058
Income	−0.015	0.022	−0.042	−0.711	0.478	0.822	1.216
Education	0.060	0.023	0.153	2.533	0.012	0.790	1.266
Age	0.003	0.002	0.063	1.117	0.265	0.903	1.108
Video	0.167	0.056	0.284	2.988	0.003	0.317	3.151
Photographs	−0.086	0.068	−0.121	−1.269	0.205	0.318	3.149
Text	0.215	0.055	0.329	3.923	0.000	0.408	2.451
VR	−0.038	0.029	−0.079	−1.323	0.187	0.800	1.250
Dependent variable: Authentic (AUT); adjusted R2 = 22.2%
(Constant)	2.598	0.203		12.800	0.000
Gender	0.034	0.051	0.038	0.657	0.512	0.944	1.059
Income	0.000	0.021	0.000	0.003	0.997	0.821	1.219
Education	0.033	0.023	0.090	1.439	0.151	0.794	1.260
Age	0.002	0.002	0.046	0.778	0.437	0.900	1.111
Video	0.082	0.056	0.144	1.447	0.149	0.310	3.226
Photographs	0.079	0.068	0.116	1.171	0.243	0.317	3.155
Text	0.124	0.055	0.199	2.266	0.024	0.402	2.488
VR	−0.006	0.028	−0.014	−0.220	0.826	0.793	1.261
Dependent variable: Pleasant Ambience (PAM); adjusted R2 = 16.6%

).

By analyzing whether multimedia formats significantly predicted SAF, over and beyond demographic characteristics, (F(8, 268) = 10.353, p < 0.001), it was found that Photographs significantly predicted SAF (β = 0.291, p < 0.05) and Text significantly predicted SAF (β = 0.191, p < 0.05). Photographs emerged as a more significant determinant of SAF in comparison with Text. On the other hand, Video and VR did not significantly predict SAF. The model explains 21.3% variability of SAF. Furthermore, when testing whether media formats significantly predicted INT (F(8, 265) = 6.007, p < 0.001), Video emerged as the only, yet marginally significant, determinant of INT (β = 0.188, p = 0.057). As prospective tourists are driven by various motives, and their primary motives differ, a plausible explanation of this finding is that those prospective tourists who may be primarily driven by, e.g., a possibility to meet new and interesting people, alleviate stress, or escape from everyday routine, do not perceive an ideal match between their motives and the possibility to fulfill them, according to the messages conveyed via multimedia formats. This accentuates the necessity for travel agencies and tour operators to examine tourists’ motives and consequently segment the market and tailor itineraries and communication strategies accordingly.

When testing whether media formats significantly influenced AUT, over and beyond demographic characteristics, the overall regression was statistically significant (F(8, 263) = 10.688, p < 0.001). It was found that Video significantly influenced AUT (β = 0.284, p < 0.05) and Text significantly predicted AUT (β = 0.329, p < 0.001), where Text emerged as a more influential determinant of AUT. On the other hand, Photographs and VR did not significantly influence AUT. In addition, among demographic characteristics, Education emerged as statistically significant predictor of AUT (B = 0.060, β = 0.153, p < 0.05), which implies that with the increase in educational level there is a slight increase in expectations among tourists related to AUT. This might imply that more educated prospective tourists pay more attention to textual content when forming expectations related to AUT. The model explains 22.2% variability of AUT. Finally, the results of the regression analysis applied to examine whether multimedia formats significantly impacted PAM, over and beyond demographic characteristics, (F(8, 262) = 7.278, p < 0.001) showed that Text significantly impacted PAM (β = 0.199, p < 0.05), whereas Video, Photographs, and VR did not significantly impact PAM. The model explains 16.6% variability of PAM.

5. Discussion

The findings of the study indicate that communication channels play a significant role in shaping tourists’ expectations regarding different characteristics of urban tourist destinations. Based on the present study, it can be concluded that the more significantly the respondents perceived traditional and digital communication channels, the higher their expectations regarding tourist primary (EPTA) and secondary tourist attractions (ESTA) were. Whereas recent research related to the formation of tourists’ expectations of the destination primarily refer to contemporary communication channels, social media above all, the findings of this reserach implied that both traditional and digital communication channels, alongside personal recommendations that the tourist had been exposed to prior to the trip, significantly contribute to shaping respondents’ expectations concerning urban tourist destinations and their physical attractions. Specifically, digital communication channels and personal recommendations before the trip prominently influenced the formation of expectations regarding primary tourist attractions. For destination management organizations and inbound tour operators, this finding implies that, in order to increase the expectations of prospective visitors and motivate them to visit a destination, they should carefully tailor content conveyed via DCCs and, most importantly, organize festivals, events, sightseeing tours, and other activities, which would exceed the expectations of existing visitors, contribute to memorable experiences and, consequently, motivate visitors to spread referrals and e-WOM. Conversely, traditional communication channels used before the trip predominantly impacted expectations regarding secondary tourist attractions, such as infrastructure, accommodation, and prices. This finding suggests a potential for destination management organizations to contribute to increased expectations of prospective visitors, and their motivation to visit a destination, by attractive and informative printed promotional material distributed during tourism fairs and well-informed employees of tourist agencies, with previous positive experience related to the destination and its secondary attractions. In this regard, organization of promotional visits to a destination, directed towards outbound tour operators and their personnel, could be beneficial for the creation of itineraries which would display the best of a destination.

Also, it was shown that the higher the importance of information acquired through digital communication channels and personal recommendations prior to the trip, the greater the expectations regarding safety, interest, authenticity, and the pleasant ambience of an urban tourist destination. In summary, the analysis underscores the significance of digital communication channels and personal recommendations in shaping and managing expectations related to the intangible characteristics of esteemed urban tourist destinations. The significance of contemporary communication channels, especially social media, is well emphasized in the literature. It was noted that social media communication, both controlled and uncontrolled by the destination organization, is especially significant in this regard [68]. The results of one qualitative study indicated that social media act as important information sources for tourists’ choices related to choice of destination, as well as transportation, accommodation, attractions, shopping, and leisure activities [105]. The findings are also in line with previous research findings, such as those of de Lima et al. [19], who indicated that visitors’ expectations are influenced by both social media and destination image, whereas word-of-mouth communication and tourists’ previous experiences shaped that image. In one earlier study, Rodríguez del Bosque et al. [42] also confirmed that tourist expectations were influenced by external communication and word-of-mouth communication. Advertising as well as word-of-mouth (WOM) recommendations were found to influence cognitive and affective image, which together form tourists’ expectations toward destinations [38]. Based on the Use and Gratifications Theory (UGT), Bu et al. [106] discovered the impact of Digital Content Marketing (DCM) on gastronomy tourism and consequently on e-WOM communication. The findings of Chavez et al. [54] showed the relationship between tourists’ travel motivations and gratifications provided by social media. The significance of UGC, which was rated with the second highest average scores by the respondents in this study, in creating expectations of the destination was also confirmed in the research of Narangajavana et al. [89]. Cheunkamon et al. [107] showed that, through direct and indirect effects, there is a clear intention of tourists to use social media when making a decision on travel planning. Chu et al. [108] explored the influence of modern media on tourist expectations and its role in the promotion of tourism and the travel industry, and pointed out the importance of digital media, online reviews, virtual reality and augmented reality, and influencers.

Besides the significant influence of digital communication channels, this study indicated the importance of traditional communication channels in shaping tourists’ expectations as well, even though respondents undoubtedly attached greater importance to digital communication channels. Some of the previous research confirmed the significant relationship between television (movies, TV dramas, and TV programs), as traditional media channels, with destination image and tourists’ decision-making process [109] and travel motivations [110].

Based on the findings related to relations between the perceived significance of multimedia formats and respondents’ expectations, it can be deduced that increased importance attached to online video content, photographs, texts, and comments correlates with heightened expectations regarding safety, interest, authenticity, and the overall pleasantness of the environment and atmosphere. Moreover, as respondents perceived virtually displayed information to be of greater significance, their expectations regarding safety and the pleasant ambience of urban tourist destinations were raised. Notably, all formats of online media content exhibited positive associations with expectations concerning all intangible characteristics of urban tourist destinations.

6. Conclusions

The concept of urban destination image stands out as the primary factor responsible for shaping the expectations associated with a destination. In summary, tourists’ expectations related to tourist destinations encompass a combination of desired experiences, services, and outcomes that tourists anticipate before making a visit. Tourists’ expectations can be understood as mental representations that tourists construct in their minds about a destination based on information sources such as travel guides, online reviews, advertisements, social media content, and recommendations from friends or family. Understanding and fulfilling these expectations are essential for creating memorable and satisfying travel experiences, fostering destination competitiveness, and enhancing overall tourist satisfaction. Tourists’ expectations are dynamic and can evolve over time as tourists gather more information from various external sources. Meeting or exceeding tourists’ expectations is crucial for destination management organizations, hospitality businesses, and tour operators to ensure customer satisfaction, positive WOM, repeat visits, and destination loyalty.

Given the limited research on expectation formation in destination marketing, this paper contributes to the existing literature by offering both theoretical insights and empirical evidence regarding the factors that influence tourist expectations. This study contributes to the Uses and Gratifications Theory by proving that the relevance attributed to different communication channels and media formats has a significant impact on tourists’ expectations related to tourist attractions presented in those media, which, subsequently, has an impact on their intentions to visit the destination which possesses those attractions. A practical justification for this research stems from the importance of understanding the mechanism of forming tourists’ expectations. The findings of this study might assist destination managers to gain a deeper understanding of the factors influencing visitors’ expectations. Without this knowledge, companies in the tourism sector and local government entities may implement actions and policies that are not aligned with tourists’ objectives and expectations. Such misalignment can lead to dissatisfaction and potentially negative perceptions of the destination, highlighting the significance of aligning strategies and offerings with tourists’ expectations for a positive tourism experience. This understanding can then be leveraged to align promotion strategies with the goal of attracting the ideal target audience. Additionally, the exploration of the communication channels and multimedia formats in this research offers valuable insights into how different types of communication channels influence urban destination images and visitor expectations.

The research presented in this paper contains certain limitations. Given the low level of explained variance in the multiple regression models, it is obvious that there are other variables that are not covered by those models, which predict the dependent variable; therefore, in following studies, the number of variables taken into account should be expanded. One of the limitations is the risk of obtaining respondents’ weak memory recall, considering that respondents nominated an urban destination they visited during the last five years and responded regarding their expectations related to that, which may have led to potential errors or inconsistencies. Another limitation is the issue of sample size. Even though the sample is representative for Belgrade, it may not accurately reflect the broader population. Thus, the results obtained in this study should not be generalized, as they might not be applicable to the general tourist population and other national frameworks. Regarding future research directions, the authors have the intention to broaden the scope and depth of the research, with the goal to explore more complex relationships and delve into nuanced perspectives. This refers particularly to the moderating role of frequency of travel and previous travel experiences when testing causal relationships.