Abstract
Purpose
To evaluate some of the current discussions about the possible impacts of social networks on the development of gastronomy in the Republic of Serbia. There could be either positive and/or negative impacts and this viewpoint provides some reflection on what the future might hold for some if not many tourism destinations in Serbia and the region when the tourism industry restarts after the pandemic of Covid-19 virus.
Design/methodology/approach
The research was conducted in December 2021, on a total of 244 respondents in three cities in Serbia. SPSS software was used, version 26.00, and the obtained data were analyzed by descriptive statistics. Then, to determine the structure of the questionnaire and the percentage of variance, an exploratory factor analysis was performed together with a higher order factor analysis, in order to obtain the desired number of factors. Subsequently, the authors used multiple regression analysis to confirm the significance of the predictors. The goal of the research was to determine whether, and to what extent, social networks can predict the choice of restaurants and gastronomic offers in Serbia. Serbian gastronomy has a great influence on the development of tourism, so this research has a wide scientific and practical contribution.
Findings
This paper provides a context and viewpoint on the possible implications of impacts of social networks on the development of gastronomy in the Republic of Serbia in the future. It has been proven that social networks can have an impact on the development of gastronomy and tourism itself.
Research limitations/implications
To examine the impact of social networks on the development of gastronomy, the authors conducted a survey online due to the current Covid-19 pandemic. The limitation of this research was precisely that the authors did not have the opportunity to conduct the research live due to the Covid-19 pandemic. It is recommended that such surveys be conducted live in direct contact with respondents in the future in order to obtain a larger sample with fully completed questionnaires.
Practical implications
The importance of social networks is increasingly a topic of study of world research, especially when it comes to gastronomy, which is becoming increasingly important as an activity in the tourism industry. The results indicate that the greatest importance in predicting the choice of restaurants and gastronomic offers has social networks and marketing. The importance of the work is reflected in the recognition of the importance of social networks, in order to better place Serbian gastronomy.
Social implications
This paper offers a synthesis of views that fosters an understanding of the possibility of impacts of social networks on the development of gastronomy in the Republic of Serbia before and after the Covid-19 pandemic.
Originality/value
The viewpoint proffered in this paper provides scope for a rapid evaluation of the current status of gastronomy tourism in Serbia which can help practitioners and researchers in the faster and better development of gastronomy and tourism.
Keywords
Citation
Vukolic, D., Gajić, T. and Penic, M. (2022), "The effect of social networks on the development of gastronomy – the way forward to the development of gastronomy tourism in Serbia", Journal of Tourism Futures, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JTF-01-2022-0034
Publisher
:Emerald Publishing Limited
Copyright © 2022, Dragan Vukolic, Tamara Gajić and Mirjana Penic
License
Published in Journal of Tourism Futures. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.
1. Introduction
Tourist demand has changed significantly in the last 20 years. Food is no longer just a basic human need but has become one of the main motivations that influence tourists to travel (Mak et al., 2017). Gastro tourism changes the attitude of the environment in which it develops and at the same time brings certain changes. Maintaining positive attitudes of all participants in gastro tourism is important for several reasons, and one of the main is the satisfaction of visitors to a particular destination (Vukolić et al., 2021a, b; Hall and Mitchell, 2007).
An important segment in the development of tourism globally is gastronomy. Gastronomy in the Balkan countries became popular after the 1990s, so as a relatively new topic in the academic literature, it is often critically monitored, which influences future development (Kukanja and Peštek, 2020). Gastronomy is not just food but a combination of experience, culture and food and beverage tasting (Mason and Paggiaro, 2012).
Gastronomic tourism is defined as an activity of research and discovery of culture and history through food, which influences the formation of unforgettable experiences (Long, 1998; Vukolić, 2020). The importance of certain food attributes and the impression that food leaves on tourists are of great importance for the development of loyalty among tourists (Gajić et al., 2021). Marketing experts emphasize that destinations must ensure that advertising or marketing campaigns significantly attract potential tourists (Yu and Sun, 2019).
In recent years, various websites, such as Facebook, Twitter, LinkedIn and Instagram, as well as various blogs that allow interaction between people with similar interests, have experienced great expansion. Younger generations of employees, as well as those yet to find themselves in the labor market, have learned to use social networking tools and use them to connect and exchange ideas, information or their attitudes and emotions (Cvetkovski et al., 2015). Given that Instagram has been tested as a marketing tool (Djafarova and Rushworth, 2017; Wally and Koshy, 2014), there are implications that it can affect the development of gastronomic destinations.
Due to the great gastronomic potential that Serbia has, as well as due to the impact of the development of gastronomic tourism on the overall development of the economy, the authors approached the research with the purpose to determine whether, and to what extent, social networks can predict the choice of restaurants and gastronomic offers in Serbia. Serbian gastronomy has a great influence on the development of tourism, so this research has a wide scientific and practical contribution. According to that fact, the authors of the paper conducted a survey, in order to determine the extent to which social networks have an impact on consumer awareness, specifically on the choice of restaurant and type of food.
The results were obtained on a total sample of 244 respondents. Undoubtedly, they show the importance of social networks, primarily Instagram and oral transmission of information, while other types of marketing are less important in managing the development of gastro tourism in Serbia. This research can contribute to raising awareness among restaurant owners and all participants in the development of gastro tourism, about the importance of the impact of social networks on the placement of their products in the Serbian market and beyond.
Therefore, the obtained results clearly show, what kind of marketing, participants in tourism should be directed to, and thus make better sales of products and make fewer mistakes and costs in their business. Therefore, in the harsh market, strong and relentless competition, such as tourism, will ensure long-term business.
2. Literature review
2.1 Social networks and gastronomy tourism
Culinary tourism or gastronomy tourism is the most popularly adopted term to describe a form of tourism that significantly emphasizes a relationship between the insider and outsider created via food as culture. The term throughout the relevant literature is used to suggest an undeniable and intrinsic link between food and culture, which makes it different from other similar terms. Culinary tourism is the experience of the “other” through food-related activities, whereby cultural learning and knowledge transfer of the destination and its people are facilitated. Food in culinary tourism is viewed as a medium of cultural experiences; accordingly, “culinary tourism” is defined by the experience of food activities and consequent cultural consumption, as well as by the desire behind the individual’s involvement (Horng and Tsai, 2010; Silkes et al., 2013).
Kaplan and Haenlein (2010) are in Concepts of Web 2.0 and UGC, proposed that social media refers to “a group of Internet-based apps that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user generated content”. Filo et al. (2015) defined social media as new media technologies facilitating interactivity and co-creation that allow development and sharing of user-generated content among and between organizations and individuals.
Obar and Wildman (2015) further reviewed the literature on social media and identified four unique dimensions, namely Web 2.0, UGC, creation and interaction and based on that definitions, social media can be summarized as Web 2.0-based online platforms that enable and promote the users’ independent creation and sharing of UGC. However, contemporary literature demonstrates lack of unified classification systems for social media (Brandtzæg, 2010; Chong et al., 2018; Gretzel, 2018).
Based on the theories in media research and social process, Kaplan and Haenlein (2010) categorized social media into six types according to their usage characteristics. The six types are collaborative projects, blogs, content communities, social networking sites, virtual game worlds and virtual social worlds. This classification has two dimensions. According to Dao (2015), the first is the degree of social presence and the richness of the medium, which determines how people communicate and interact with each other on the Internet. The second dimension describes social media as a tool of self-presentation and self-disclosure, which involves impression management and public sharing of personal information (Goffman, 1959; Derlega and Grzelak, 1979).
With the advent of information and communication technologies, tourists rely on current and fast information they receive through social networks, online reviews, blogs and others before traveling (Buonincontri et al., 2017). People today produce content themselves and update information online through a form of user content (Fatanti and Suyadnya, 2015). Companies can use social networks to interact with users on networks and to work together to create unique services and share travel experiences (Prahalad and Ramaswamy, 2004). One of the new and popular tools in digital tourism is Instagram, which has successfully evolved into a platform for advertising, promotion and marketing (Doolin et al., 2002; Zadeh and Sharda, 2014).
In order to effectively attract the interest and attention of people on social networks, attention must be paid to the popularity of the publication, which has a great impact on business success (Swani et al., 2017). The popularity model of posts suggests that the presentation, links, content, frequency and comments of the post are critical factors related to the user’s “liking” (De Vries et al., 2012). In recent years, destination marketing, branding and e-Tourism have been widely debated among academics. Despite acknowledging its importance, most of the literature has focused on Facebook and TripAdvisor (Sabate et al., 2014; Mariani et al., 2016; Fong et al., 2017).
More and more destinations are striving for branding, in order to build a reputation through various media (Sevin, 2014). Destination branding has to do with local people and local government (Chen, 2012). Overall, visitor preferences and attitudes toward the destination are key factors that directly affect success (Qu et al., 2011) and then shape the image of the destination that may be related to gastronomy (Lai et al., 2018). It has been proven that the image of local gastronomy influences the intentions of tourists to visit that destination (Ab Karim and Chi, 2010), i.e. food consumption has recently become one of the main motivations for people to travel (Mak et al., 2017; Ćamilović, 2020).
Although the concept of destination image through gastronomy is still new in the academic community (Lai et al., 2018), the motivation of gastronomic tourists is of growing interest to researchers. Fields (2002) found that food can be a different motive. This is related to the fact that food can be a physical or cultural motive, but also a status symbol. Kim et al. (2009) investigated how food can still motivate people. They were guided by the opinion that food can also be an exciting experience.
A survey conducted in mainland China (considered one of the fastest growing and most influential markets for the use of social networks by tourists) on a sample of 406 respondents aimed to prove the use and trust of Chinese social networks in the travel planning process. The authors came to the conclusion that destination marketing organizations should keep in mind that Chinese tourists like to use networks like Instagram, Facebook and others to plan trips. The same authors also point out that social networks are a powerful tool for the development of tourism, and that in China the use of these networks is stimulated by the development of communication technology and tourism (Yuan et al., 2022).
Research by Dossena et al. (2021) suggests that the theoretical framework of social media could be strategically used for different goals by relying on specific capabilities and competencies. The authors tested it and found that, though nowadays restaurant managers mainly focus on a narrow set of social media competencies linked to relational and marketing capabilities, some also rely on social media to promote organizational change and innovation.
2.2 Gastronomic tourists and social networks
The study by Jiménez Beltrán et al. (2016), concludes that overall satisfaction with the visit is related to local gastronomy. According to Lai et al. (2018), as a whole food opinion can be analyzed by demand (tourists) or by supply (service providers). In China, more and more cities are using social media to market and promote their products and services online (Zhou and Wang, 2014). Previous data suggest that such practices provide great benefits to societies, through interaction with the community in marketing campaigns (Mangold and Faulds, 2009; Krstić and Đurđević, 2017; Chaney and Ryan, 2012). Zhou and Wang (2014), in their research, pointed out that compared to the traditional approach, social networks are a more convenient and transparent platform for potential tourists.
The term, user-generated content, refers to any form such as images, videos or texts that users post via social media (Fatanti and Suyadnya, 2015). Authors Burgess et al. (2009) point out that people post reviews, comments and feedback, as well as share information but also communicate with each other online. In recent years, social media posts have been increasingly used as an online marketing tool by local governments, travel organizations and travel agencies (O'Reilly, 2005). It is important to note that tourism is ranked as the largest industry in terms of the volume of online transactions (Mack et al., 2008; Vukolić et al., 2021c).
Authors Saboo et al. (2016) found that consumers check the quality of products or services on social networks before buying anything. Chua and Banerjee (2015) indicate that the interaction with consumers is significantly influenced by posts (comments and other feedback) on social networks, both during and after the trip, which has a great impact on further business. Travel experiences, as such, can be created together in a group of multiple people, consumers or tourists (Prahalad and Ramaswamy, 2004).
A study conducted in 2017, collected data from 385 tourists and indicated that joint experience creation can lead to greater satisfaction with a destination. Study results also revealed that the attitude of tourists toward sharing their experiences with others does not influence experience co-creation (Buonincontri et al., 2017). The best examples of well-known social networks are Instagram, Facebook, TripAdvisor and the YouTube channel. Previous research has analyzed travel reviews on TripAdvisor to investigate how comments on that network affect the travel planning process of travelers. Online reviews have been found to influence accommodation decisions more than the entire travel route (Gretzel and Yoo, 2008).
Another study investigated hotel ratings and tourist feelings after online reviews on TripAdvisor (Fong et al., 2017). Mariani et al. (2016) explored how Facebook can be strategically used in destination marketing in the case of Italy by conducting big data analysis and semi-structured interviews while Sabate et al. (2014) examined Facebook regarding the creation of effective branding strategies through the use of social networks. Results suggest that the richness of the content (inclusions of images and videos) raises the impact of the post in terms of likes. On the other hand, using images and a proper publication time are significantly influencing the number of comments, whereas the use of links may decrease this metric (Sabate et al., 2014).
A newer marketing tool in digital tourism is Instagram (Hanan and Putit, 2014), which is getting more and more attention from researchers (Wally and Koshy, 2014). This is a photo-sharing platform created in 2010. It is interesting that the Instagram of companies, catering facilities, tourist organizations and agencies joined only a few years ago (Virtanen et al., 2017; Garabinović and Milićević, 2021).
Instagram has become an effective business tool to reach consumers and tourists as soon as possible (Miles, 2014). Service providers can communicate with their consumers and get their support through Instagram, in a very short time. This network is gaining in popularity because it has about 100 million active users per month worldwide with 5 million posts shared every day (Miles, 2014). Subrahmanyam et al. (2008) point out that the main users of Instagram are people aged 18 to 34.
2.3 How to reach the target group – comments, likes and communication
Some of the previous literature has examined how Instagram helps promote and brand a destination through photography in the case of Indonesia (Fatanti and Suyadnya, 2015). Djafarova and Rushworth (2017) surveyed women who use Instagram between the ages of 18 and 30, in order to find out if it has anything to do with buying products and services with celebrities. They came to the result that women of the mentioned age buy more if a certain product is offered by a famous person, regardless of the brand of that product or service.
In the case of social media, the link or link inserted in the post is more interactive with network users compared to content that contains only texts (Fortin and Dholakia, 2005; Gajić et al., 2020). The situation is similar to posts that have geotags or hashtags. Their function is the same as the links because URLs cannot be clicked in posts or comments on Instagram yet. The interactivity of posts must be taken into account because consumers prefer to interact with certain brands and often ask questions through social networks (Lin et al., 2017), and it is a key feature in generating posts (Chua and Banerjee, 2015). Taylor et al. (2011) have shown that posts that are considered fun and exciting have a positive impact on consumer attitudes. Moreover, consumers prefer informative posts driven by the need to seek information to motivate shopping (Lin et al., 2017).
Posts also require a certain competence of marketing professionals in the organization of the format (Zadeh and Sharda, 2014). Lin et al. (2017) have proven that the length of the post directly affects the interests of users. Mariani et al. (2016) claim that on Facebook, long posts were more favorable, while Sabate et al. (2014) say that posts with less than 80 characters are more influential in interacting with social media visitors. It is very important to note that the time of publishing a certain post is significant.
Mariani et al. (2016) found that publications published in the early morning hours are less popular compared to publications published during the largest number of consumer activities in the evening (Lin et al., 2017). In this regard, the authors Mariani et al. (2016) point out that posting too often is inefficient and can negatively affect sales or promotion. The usefulness of comments on social networks refers to how positive or negative the comments are. Previous research has indicated that positive comments lead to greater popularity (De Vries et al., 2012), due to the emergence of positive emotions among social media users (Bickart and Schindler, 2001). The more comments, the more subsequent comments will probably be generated and lead to greater popularity of posts (Moe and Trusov, 2011).
While likes indicate users’ interest in a particular post, commenting suggests a big impact because users are asked to dedicate their time (Sabate et al., 2014), and implies greater engagement of social media users (Swani et al., 2017). Because consumers like to communicate with other users through social networks, this can be used to create experiences with potential consumers together (Buonincontri et al., 2017; Prahalad and Ramaswamy, 2004).
Drummey (2017) warns that in addition to the quality content of the campaign itself, it is equally important to choose the right social network through which organizations will reach the desired target group. Although no quantitative results were provided, study authors Bartle et al. (2013) found that the process of information sharing among social network members could play a functional role in the diffusion of instrumental travel information. It also could play a social role whereby perceived in-group membership (i.e. those who cycle to work) and high levels of trust reinforce positive views of cycling.
While social networking can help strengthen guest loyalty and satisfaction, it can also lead to unfair negativity, inaccurate information and undue criticism (Kasavana, 2008).
The main problem encountered in using social media is that customers’ opinions, thoughts and expressions are not well presented that leads to bad impressions and unfair criticism (Buted et al., 2014).
Based on the given literature and similar research, the authors started from the initial hypothesis:
Social networks have a strong predictive power to determine the score on a criterion variable.
Oral recommendation has strong predictive power to determine score on criterion variable.
The second type of marketing has a strong predictive power to determine the score on the criterion variable.
3. Methodology
3.1 Participants and procedure
The authors performed empirical research by collecting and processing primary data, on a sample of a total of 244 respondents, in three cities in Serbia: Niš, Novi Sad and Vrnjačka Banja, in December 2021. A total of 500 questionnaires were distributed, but due to the validity of the answers, only questionnaires with complete answers were taken into the analysis. Of the 500 questionnaires, 256 were not used because they did not have complete answers.
It is considered that this is because the survey was conducted online, so there was no possibility to check the questionnaire directly with the respondents. The questionnaire was constructed, modified and adapted so that the authors could use several other scientific researches (Cvetkovski et al., 2015; Gajić et al., 2020; Vukolić et al., 2021b). About 43.2% of men and 56.8% of women participated in the research. The largest percentage of research participants have a university degree, that is, 59.3% of them, while a total of 32.6% have a high school diploma and 8.1% have a PhD degree.
Looking at the monthly earnings of survey participants, the data are as follows: a total of 11.8% earns up to 200 euros per month, 42.7% of them earn from 200 to 500 euros per month and 45.5% of those earn from 500 to 1,000 euros per month. SPSS software, version 26.00, was used to process the obtained data.
3.2 Measures and statistical analysis
SPSS software, version 26.00, was used to process the obtained data. A three-point scale was used to evaluate the given items. A descriptive statistical analysis was performed in order to see the descriptive values of the given items. Then, the authors first approached the grouping of all items with the help of exploratory factor analysis.
Exploratory multivariate data analysis is the unification of research data analysis and multivariate data analysis. Reducing the number of variables in the analysis when there are too many, with some of them “overlapping”, because they have similar meaning and behavior. Factor analysis is an interdependence technique because it looks for a group of variables that are similar in the sense that they “move together” and therefore have great interdependence. When one variable has a large value, then the other variables in the group have a large value. Since the factors are statistically separated, all factors are initially unrelated (orthogonal). This simplifies the understanding of a wide range of variables that describe a category from the service or manufacturing sector.
A framework for further data analysis is also being created. Then, a higher order factor analysis was performed to obtain the desired number of factors and the following analysis was performed. After determining the number of factors, the authors performed a multiple regression analysis to determine whether the relevant factors can have a strong predictive power in determining the score on the criterion variable restaurant selection and gastro offer in Serbia. If the problem we are observing can be treated as a problem of one dependent and several independent variables, this is a suitable situation for data analysis by multiple regression. If the connection between them is linear, the case boils down to a multiple linear model. Each corporate site provides the opportunity for users to check the content or offer. A ComScore report shows that 45% of tourists download photography or comment on social networks after using the service (http://www.comscore.com/FutureinFocus2013).
4. Results and discussion
The parametric methodology was used, because all values were normally distributed. Skewness and Kurtosis values range from minus 1 to plus 1, which are values of normal data distribution. The value of Cronbach’s alpha is 0.967, which makes the questionnaire very reliable.
Table 1 shows the descriptive values for the given items, which concern the influence of social networks on the gastronomic offer in Serbia. It is noticed that social networks like Instagram have a great influence on visiting restaurants and choosing gastronomic products (m = 2,335, sd = 0.577). Facebook is rated slightly worse: m = 1.65, sd = 0.924. A certain part of the research participants thinks that it is the best recommendation from a friend, because they rate this item with an average grade of m = 2.09.
Respondents believe that Instagram gives the best recommendations, for luxury restaurants and offers, but also that it is reliable and dedicated mainly to foreign visitors. Also, a large part of respondents point out that price plays a role in choosing restaurants and gastronomic offers (m = 2.41). Losses were rated television commercials (m = 1.11), followed by newspaper offers (m = 1.98), as well as flyers distributed on city streets (m = 1.44).
The Kaiser–Meyer–Olkin (KMO) and Bartlett’s test (Table 2) is a test conducted to examine the strength of a partial correlation (as factors explain each other) between variables. The KMO test is a measure of how suitable your data is for factor analysis. KMO values closer to 1.0 are considered ideal, while values less than 0.5 are unacceptable. The obtained value of 0.981 can be assessed as very good.
The factor structure matrix presented in Table 3 contains factor loads that represent the correlation coefficients between the selected factors and variables. Factor loads indicate the importance of each variable for each factor. All saturations less than 1 are not taken into account. All items are classified into seven factors. The first factor has the highest saturation of 18.436%. The model explains a total of 75.8% of the variance.
The Scree test, which was used to determine the number of factors that should be included in the final version, identified a model with also seven factors as the optimal model. The number of dimensions corresponding to the number of points before the fracture is retained (Figure 1).
Due to the difficulty of adding names to factors due to different items, the authors performed a higher order factor analysis or hierarchical factor analysis, in order to obtain only three desired factors (Table 4), whose predictor impact will be determined by regression analysis.
Data from the hierarchical factor analysis indicate that 57% of the variance was explained with three obtained factors, which is more than half of the explained variance and is very acceptable. Three factors were obtained: the impact of word-of-mouth advertising, the impact of social networks and other ways of marketing. Then, the authors approached a multiple regression analysis, to determine whether the given factors can be strong predictors in predicting the score on the criterion variable: restaurant selection and gastronomic offer in the Republic of Serbia.
Table 5 shows the percentage explanation of variance. It is noticed that this model fits and explains 67% of the variance. This is what R-square says, as a coefficient of multiple determination. Coefficient R is the coefficient of multiple correlation and indicates how many predictors together correlate with the criterion variable, in this case, the variable of the required innovation.
Furthermore, in the ANOVA table (Table 6) it can be noticed that there is statistical significance in the predictor power of the factors on the prediction of the criterion variable. Given the statistical significance, a model of multiple regression analysis can be further developed to see the partial contribution of each of the given items to the criterion variable.
The table of multiple regression analysis (Table 7) shows the partial contribution of each of the factors and the statistical significance in predicting the choice of restaurants and gastronomic offers. Parameter B represents the standardized partial contribution, more precisely whether that predictor itself has significance in the prediction.
Beta is interpreted as a correlation of how much the predictor is related to the criterion variable. It is noticed that the greatest influence on the criterion variable has the factor influence of social networks with the following values: p = 0.00, B = 0.535, β = 0.578. The choice of restaurants and gastronomic offers significantly predict oral advertising: p = 0.23, B = 0.213, β = 0.154.
However, other types of advertising do not show statistical significance in predicting the criterion variable. The first two hypotheses H1 and H2 have confirmed that social networks have a predictor of power on consumer behavior in choosing restaurants and agro offers, as well as the fact that Instagram has an advantage in this. While the third hypothesis H3 is denied: the second type of marketing has a strong predictive power to determine the score on the criterion variable.
The results undoubtedly show that social networks are an important segment in the promotion of gastronomy and thus can influence the development of gastronomy and gastronomic tourism. It is recommended that future researchers see the extent to which each network can individually contribute to the development of gastronomy and tourism itself.
Although the topic of this paper is social networks and their impact on the gastronomy of the Republic of Serbia, the questionnaire was distributed through social networks and email addresses of respondents that were collected in cooperation with employees in restaurants. The limitation of this research was precisely that the authors did not have the opportunity to conduct the research live due to the Covid-19 pandemic. It is recommended that such surveys be conducted live in direct contact with respondents in the future in order to obtain a larger sample with fully completed questionnaires.
Future study may also be conducted using other variables to further confirm the result of this study.
5. Conclusion
Today, gastronomic tourism is a global trend as it increasingly attracts the interests of the public. Social networks have become a very popular medium in recent years. There are similar surveys in the Republic of Serbia, where it is claimed that over half of the respondents in the survey pointed out that they get information through social networks and a large number of them get information using online newspapers and portals as a source for their information.
As social networks that they use most often, young people in Serbia pointed out Instagram, WhatsApp, YouTube and Facebook, while other networks were pointed out in a slightly smaller percentage, but they certainly visit and use their content. The largest number of users of social networks has great confidence in their content. Tourism and gastronomy are among the most visited blogs when it comes to social networks.
Over 80% of the world’s tourists start their journey online. The classic agency tour and information gathering are being replaced by the Google homepage and social media. Today, tourism cannot be imagined without the Internet, which has brought a real revolution in the further development of this sector. According to statistics, a large 87% of millennials use social media to research travel, and the information they collect through social media is used to help them choose their preferred location. Also, ratings and reviews often play a crucial role in any travel decision, as people are more willing to trust a brand if others rate it highly, especially where the review is reliable. It is probably unnecessary to talk about how we are very happy to share photos from our travels on social networks.
Because such images provide the most realistic impressions from the trip and are perhaps the most relevant indicators of a resort and destination. Communication on social networks is two-way. In this way, organizations are enabled to improve their relationships with consumers. This is not easy, because everyone can express their opinion on the networks, which can be negative. By successfully managing the corporate profile/page of organizations, this can be turned to their advantage: potential problems can be resolved and negative opinions can be turned into praiseworthy.
Quality content loses its importance if it is in the wrong place and if it leads to a negative reaction from the users of a certain social network. Therefore, it is extremely important to choose one or more platforms for the implementation of the campaign, depending on the activities of the organization and its products or services, as well as the characteristics of the network itself and what makes it unique. In addition, it is necessary to set measurable goals. In accordance with the above research and facts, the authors conducted a field study, in order to determine to what extent and which social networks have the greatest impact on the awareness of consumers of gastronomic tourism in Serbia.
The purpose of conducting this type of factor analysis was to reduce the number of factors to three, in order to easily determine the impact of these factors on predicting consumer behavior. In this case, the three factors identified were the impact of word-of-mouth advertising, the impact of social networks and other ways of marketing. By multiple linear regression, the authors determined the predictor strength of each of the factors, to determine the nearing criterion variable choice of restaurants and gastronomic offers.
The results showed that the social network Instagram and word-of-mouth have significant power in predicting consumer behavior in choosing restaurants and gastronomic offers. Other forms of advertising have weaker predictor power. This research can have wider societal, social, scientific and practical significance. The limitations during the research are exclusively in the impossibility of live surveys of the respondents due to the pandemic of the Covid-19 virus. It is suggested that in future research, researchers collect live data and analyze individually the promotion of gastronomy on each of the current social networks.
Figures
Descriptive statistics for items
| Items | m | sd |
|---|---|---|
| I prefer to accept recommendations for restaurants and food from friends | 2.09 | 0.784 |
| I check everything on Instagram | 2.35 | 0.577 |
| I mostly follow advertisements about restaurants and gastronomic offer | 2.12 | 0.638 |
| I follow advertising emails | 1.89 | 0.901 |
| I follow blogs | 1.68 | 0.860 |
| I follow events and gastronomic events in the city | 1.32 | 0.821 |
| Social networks promote mostly foreign cuisine | 1.37 | 0.635 |
| Instagram gives the best recommendations | 2.52 | 0.817 |
| I’m looking forward to long posts on social media | 1.88 | 0.693 |
| Social networks only give expectations | 1.94 | 0.899 |
| I get the best ads through the Facebook network | 1.65 | 0.924 |
| I watch TV commercials mostly | 1.11 | 0.769 |
| I collect flyers and follow the gastronomic offer | 1.44 | 0.790 |
| Social networks unrealistically present gastro products | 1.69 | 0.802 |
| Mostly luxury restaurants are presented | 2.24 | 0.830 |
| Social networks are only good for foreign visitors | 2.26 | 0.805 |
| The price most influences the choice of gastronomic offers | 2.41 | 0.736 |
| Social networks show quality gastronomic offers | 1.90 | 0.981 |
| I follow influencers and their offer | 2.87 | 0.931 |
| I follow the offer in the newspaper | 1.98 | 0.943 |
Source(s): Authors’ research; *m = arithmetic means; sd = standard deviation
KMO and Bartlett’s test
| Kaiser–Meyer–Olkin measure of sampling adequacy | 0.981 | |
| Bartlett’s test of sphericity | Approx. Chi-square | 2560.730 |
| Df | 190 | |
| Sig. | 0.000 | |
Source(s): Authors’ research
Total variance explained
| Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadingsa | ||||
|---|---|---|---|---|---|---|---|
| Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | Total | |
| 1 | 3.687 | 18.436 | 18.436 | 3.687 | 18.436 | 18.436 | 2.944 |
| 2 | 3.434 | 17.169 | 35.605 | 3.434 | 17.169 | 35.605 | 3.071 |
| 3 | 2.275 | 11.377 | 46.982 | 2.275 | 11.377 | 46.982 | 2.603 |
| 4 | 1.825 | 9.123 | 56.105 | 1.825 | 9.123 | 56.105 | 2.591 |
| 5 | 1.481 | 7.404 | 63.510 | 1.481 | 7.404 | 63.510 | 2.237 |
| 6 | 1.281 | 6.404 | 69.914 | 1.281 | 6.404 | 69.914 | 1.993 |
| 7 | 1.179 | 5.896 | 75.810 | 1.179 | 5.896 | 75.810 | 2.237 |
| 8 | 0.985 | 4.926 | 80.736 | ||||
| 9 | 0.674 | 3.372 | 84.107 | ||||
| 10 | 0.516 | 2.582 | 86.689 | ||||
| 11 | 0.479 | 2.397 | 89.086 | ||||
| 12 | 0.436 | 2.179 | 91.265 | ||||
| 13 | 0.387 | 1.934 | 93.199 | ||||
| 14 | 0.301 | 1.506 | 94.705 | ||||
| 15 | 0.275 | 1.374 | 96.079 | ||||
| 16 | 0.230 | 1.148 | 97.227 | ||||
| 17 | 0.184 | 0.918 | 98.144 | ||||
| 18 | 0.155 | 0.777 | 98.922 | ||||
| 19 | 0.128 | 0.639 | 99.560 | ||||
| 20 | 0.088 | 0.440 | 100.000 | ||||
Note(s): Extraction method: principal component analysis
a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance
Source(s): Authors’ research
Total variance explained hierarchical factor analysis
| Component | Initial eigenvalues | Extraction sums of squared loadings | ||||
|---|---|---|---|---|---|---|
| Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | |
| 1 | 1.412 | 57.050 | 57.050 | 1.412 | 57.050 | 57.050 |
| 2 | 0.989 | 32.954 | 80.004 | |||
| 3 | 0.600 | 19.996 | 100.00 | |||
Note(s): Extraction method: principal component analysis
Source(s): Authors’ research
Model summary
| Model | R | R-square | Adjusted R-square | Std. error of the estimate |
|---|---|---|---|---|
| 1 | 0.823a | 0.677 | 0.648 | 0.378 |
Note(s): a. Predictors: factors
Source(s): Authors’ research
ANOVAa
| Model | Sum of squares | df | Mean square | F | Sig | |
|---|---|---|---|---|---|---|
| 1 | Regression | 66.890 | 20 | 3.345 | 23.448 | 0.000b |
| Residual | 31.950 | 224 | 0.143 | |||
| Total | 98.841 | 244 | ||||
Note(s): a. Dependent variable: choice of restaurants and gastronomic offers;
b. Predictors: (Constant), factors
Source(s): Authors’ research
Coefficientsa
| Model | Unstandardized coefficients | Standardized coefficients | t | Sig | ||
|---|---|---|---|---|---|---|
| B | Std. error | Beta | ||||
| 1 | (Constant) | 1.175 | 0.183 | 6.417 | 0.000 | |
| The impact of word-of-mouth advertising | 0.213 | 0.093 | 0.154 | 2.301 | 0.023 | |
| The impact of social networks | 0.535 | 0.062 | 0.578 | 8.668 | 0.000 | |
| Other ways of marketing | 0.034 | 0.059 | 0.038 | 0.574 | 0.567 | |
Note(s): a. Dependent variable: choice of restaurants and gastronomic offers
Source(s): Authors’ research
References
Ab Karim, S. and Chi, C.G.Q. (2010), “Culinary tourism as a destination attraction: an empirical examination of destinations' food image”, Journal of Hospitality Marketing and Management, Vol. 19 No. 6, pp. 531-555, doi: 10.1080/19368623.2010.493064 (accessed 14 Januray 2022).
Bartle, C., Avineri, E. and ChatterjeeK (2013), “Online information-sharing: a qualitative analysis of community, trust and social influence amongst commuter cyclists in the UK”, Transportation Research F: Traffic Psychology and Behaviour, Vol. 16, pp. 60-72, doi: 10.1016/j.trf.2012.08.013 (accessed 17 April 2022).
Bickart, B. and Schindler, R.M. (2001), “Internet forums as influential sources of consumer information”, Journal of Interactive Marketing, Vol. 15 No. 3, pp. 31-40, doi: 10.1002/dir.1014 (accessed 15 Januray 2022).
Brandtzæg, P.B. (2010), “Towards a unified Media-User Typology (MUT): a meta-analysis and review of the research literature on media-user typologies”, Computers in Human Behavior, Vol. 26, pp. 940-956.
Buonincontri, Morvillo, A., Okumus, F. and van Niekerk, M. (2017), “Managing the experience co-creation process in tourism destinations: empirical findings from Naples”, Tourism Management, Vol. 62, pp. 264-277, doi: 10.1016/j.tourman.2017.04.014 (accessed 8 Januray 2022).
Burgess, S., Sellitto, C., Cox, C., and Buultjens, J. (2009), “User-generated content (UGC) in tourism: benefits and concerns of online consumers”, pp. 1-15, 17th European Conference on Information Systems Verona, Italy.
Buted, R.D., Gillespie, S.N., Conti, B.J., Delgado, A.B., Mae, P.R., Sher, M., Rubico, A.K. and Felicen, S.S. (2014), “Effects of social media in the tourism industry of Batangas province”, Asia Pacific Journal of Multidisciplinary Research, Vol. 2 No. 3, pp. 123-131.
Ćamilović, D. (2020), “Kako lokalne turističke organizacije u Srbiji koriste Fejsbuk”, Turističko Poslovanje, Vols 25-26, pp. 27-37, doi: 10.5937/turpos0-28679 (accessed 1 April 2022).
Chaney, S. and Ryan, C. (2012), “Analyzing the evolution of Singapore's World Gourmet Summit: an example of gastronomic tourism”, International Journal of Hospitality Management, Vol. 31 No. 2, pp. 309-318, doi: 10.1016/J.IJHM.2011.04.002 (accessed 16 Januray 2022).
Chen, N. (2012), “Branding national images: the 2008 Beijing summer olympics, 2010 Shanghai world expo, and 2010 Guangzhou asian games”, Public Relations Review, Vol. 38 No. 5, pp. 731-745, doi: 10.1016/j.pubrev.2012.04.003 (accessed 25 Januray 2022).
Chong, A.Y.L., Khong, K.W., Ma, T., McCabe, S. and Wang, Y. (2018), “Analyzing key influences of tourists' acceptance of online reviews in travel decisions”, Internet Research, Vol. 28 No. 3, pp. 564-586, doi: 10.1108/IntR-05-2017-0212 (accessed 5 March 2022).
Chua, A.Y.K. and Banerjee, S. (2015), “How businesses draw attention on facebook through incentives, vividness and interactivity”, International Journal of Computer Science, Vol. 42 No. 3, pp. 275-281.
Cvetkovski, T., Langović-Milićević, A. and Babić, S. (2015), “Primena društvenih mreža u regrutovanju kandidata za upražnjena radna mesta”, Godišnjak Fakulteta Za Kulturu I Medije: Komunikacije, Mediji, Kultura, Vol. 7, pp. 289-304, doi: 10.5937/gfkm1507289C (accessed 30 Januray 2022).
Dao, D.V. (2015), “Social media classification scheme in online teaching and learning activities: a consideration for educators”, International Journal of Education and Social Science, Vol. 2 No. 4, pp. 85-94.
De Vries, L., Gensler, S. and Leeflang, P.S. (2012), “Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing”, Journal of Interactive Marketing, Vol. 26 No. 2, pp. 83-91, doi: 10.1016/j.intmar.2012.01.003 (accessed 5 Januray 2022). (accessed 19 Januray 2022).
Derlega, V.J. and Grzelak, J. (1979), “Appropriateness of self-disclosure”, in Chelune, G.J. (Ed.), Selfdisclosure: Origins, Patterns, and Implications of Openness in Interpersonal Relationships, Jossey-Bass, San Francisco, pp. 151-176.
Djafarova, E. and Rushworth, C. (2017), “Exploring the credibility of online celebrities' Instagram profiles in influencing the purchase decisions of young female users”, Computers in Human Behavior, Vol. 68, pp. 1-7, doi: 10.1016/j.chb.2016.11.009 (accessed 13 Januray 2022).
Doolin, B., Burgess, L. and Cooper, J. (2002), “Evaluating the use of the web for tourism marketing: a case study from New Zealand”, Tourism Management, Vol. 23 No. 5, pp. 557-561.
Dossena, C., Mochi, F., Bissola, R. and Imperatori, B. (2021), “Restaurants and social media: rethinking organizational capabilities and individual competencies”, Journal of Tourism Futures, Vol. 7 No. 1, pp. 20-39, doi: 10.1108/JTF-06-2019-0050 (accessed 17 February 2022).
Drummey, C. (2017), “Influencer and marketing campaigns on social media: which platforms to use and when”, available at: https://www.marketingprofs.com/articles/2017/31671/influencerand-marketing-campaigns-on-social-media-whichplatforms-to-use-and-when?adref=nlt022717 (accessed 5 Januray 2022).
Fatanti, M.N. and Suyadnya, I.W. (2015), “Beyond User Gaze: how Instagram creates tourism destination brand?”, Procedia - Social and Behavioral Sciences, Vol. 211, pp. 1089-1095, doi: 10.1016/j.sbspro.2015.11.145 (accessed 22 Januray 2022).
Fields, K. (2002), “Demand for the gastronomy tourism product: motivational factors”, Tourism and Gastronomy, 1st ed. pp. 50-64, eBook ISBN9780203218617.
Filo, K., Lock, D. and Karg, A. (2015), “Sport and social media research: a review”, Sport Management Review, Vol. 18 No. 2, pp. 166-181, doi: 10.1016/j.smr.2014.11.001 (accessed 2 Januray 2022).
Fong, L.H.N., Lei, S.S.I. and Law, R. (2017), “Asymmetry of hotel ratings on TripAdvisor: evidence from single- versus dual-valence reviews”, Journal of Hospitality Marketing and Management, Vol. 26 No. 1, pp. 67-82, doi: 10.1080/19368623.2016.1178619 (accessed 7 Januray 2022).
Fortin, D.R. and Dholakia, R.R. (2005), “Interactivity and vividness effects on social presence and involvement with a web-based advertisement”, Journal of Business Research, Vol. 58 No. 3, pp. 387-396, doi: 10.1016/S0148-2963(03)00106-1 (accessed 9 Januray 2022).
Gajic, T., Radovanovic, M., Tretiakova, T. and Syromiatnikova, J. (2020), “Creating brand confidence to gastronomic consumers through social networks–a report from Novi Sad”, Journal of Place Management and Development. Vol. 14 No. 1, pp. 32-42, ISSN: 1753-8335, doi: 10.1108/JPMD-04-2020-0033 (accessed 1 March 2022).
Gajić, T., Popov Raljić, J., Blešić, I., Aleksić, M., Vukolić, D., Petrović, M.D., Yakovenko, N.V. and Sikimić, V. (2021), “Creating opportunities for the development of craft beer tourism in Serbia as a new form of sustainable tourism”, Sustainability, Vol. 13 No. 16, p. 8730, doi: 10.3390/su13168730 (accessed 10 Januray 2022).
Garabinović, D. and Milićević, S. (2021), “Internet promocija Moravičkog upravnog okruga kao turističke destinacije”, Ekonomika, Vol. 67 No. 1, pp. 27-41, doi: 10.5937/ekonomika2101027G (accessed 5 February 2022).
Goffman, E. (1959), The Presentation of Self in Everyday Life, Doubleday, Garden City, NY.
Gretzel, U. (2018), “Tourism and social media”, The Sage Handbook of Tourism Management, Sage, Los Angeles.
Gretzel, U. and Yoo, K.H. (2008), “Use and impact of online travel reviews”, Information and Communication Technologies in Tourism, Laboratory for Intelligent Systems in Tourism Department of Recreation, Park, and Tourism Sciences Texas A&M University, pp. 35-46, doi: 10.1007/978-3-211-77280-5_4 (accessed 22 Januray 2022).
Hall, C.M. and Mitchell, R. (2007), “Gastronomic tourism: comparing food and wine tourism experiences”, Niche Tourism, Contemporary Issues, Trends and Cases, pp. 73-88, doi: 10.4324/9780080492926 (accessed 16 January 2022).
Hanan, H. and Putit, N. (2014), “Express marketing of tourism destinations using Instagram in social media networking”, Hospitality and Tourism: Synergizing Creativity and Innovation in Research, Taylor & Francis Group, pp. 471-474.
Horng, J. and Tsai, C. (2010), “Government websites for promoting East Asian culinary tourism: a cross-national analysis”, Tourism Management, Vol. 31 No. 1, pp. 74-85, doi: 10.1016/j.tourman.2009.01.009 (accessed 20 April 2022).
Jiménez Beltrán, J., López-Guzmán, T. and Santa-Cruz, F.G. (2016), “Gastronomy and tourism: profile and motivation of international tourism in the city of córdoba, Spain”, Journal of Culinary Science and Technology, Vol. 14 No. 4, pp. 347-362, doi: 10.1080/15428052.2016.1160017 (accessed 8 Januray 2022).
Kaplan, A.M. and Haenlein, M. (2010), “Users of the world, unite! the challenges and opportunities of social media”, Business Horizons, Vol. 53 No. 1, pp. 59-68, doi: 10.1016/j.bushor.2009.09.003 (accessed 6 Januray 2022).
Kasavana, M.L. (2008), “The unintended consequences of social media and the hospitality industry”, Hospitality Upgrade, available at: http://www.hospitalityupgrade.com/files/File_Articles/HUFall08KasavanaSocialMediaandHospitality.pdf.
Kim, Y., Eves, A. and Scarles, C. (2009), “Building a model of local food consumption on trips and holidays: a grounded theory approach”, International Journal of Hospitality Management, Vol. 28 No. 3, pp. 423-431, doi: 10.1016/j.ijhm.2008.11.005 (accessed 2 March 2022).
Krstić, A. and Đurđević, B. (2017), “Marketing na društvenim mrežama”, Marketing, Vol. 48 No. 4, pp. 254-260, doi: 10.5937/Markt1704254K (accessed 7 Januray 2022).
Kukanja, M. and Peštek, A. (2020), “Development of Slovenia's gastronomy – from peasant food to the European region of gastronomy 2021”, in Peštek, A., Kukanja, M. and Renko, S. (Eds), Gastronomy for Tourism Development, Emerald Publishing Limited, Bingley, pp. 109-131, doi: 10.1108/978-1-78973-755-420201007 (accessed 9 Januray 2022).
Lai, M.Y., Khoo-Lattimore, C. and Wang, Y. (2018), “A perception gap investigation into food and cuisine image attributes for destination branding from the host perspective: the case of Australia”, Tourism Management, Vol. 69, pp. 579-595, doi: 10.1016/j.tourman.2018.06.033 (accessed 12 Januray 2022).
Lin, H., Swarna, H. and Bruning, P.F. (2017), “Taking a global view on brand post popularity: six social media brand post practices for global markets”, Business Horizons, Vol. 60 No. 5, pp. 621-633, doi: 10.1016/j.bushor.2017.05.006 (accessed 1 March 2022).
Long, M.L. (1998), “Culinary tourism: a Folkloristic perspective on eating and otherness”, Southern Folklore.
Mack, R.W., Blose, J.E. and Pan, B. (2008), “Believe it or not: credibility of blogs in tourism”, Journal of Vacation Marketing, Vol. 14 No. 2, pp. 133-144, doi: 10.1177/1356766707087521 (accessed 19 February 2022).
Mak, A.H.N., Lumbers, M., Eves, A. and Chang, R.C.Y. (2017), “The effects of food-related personality traits on tourist food consumption motivations”, Asia Pacific Journal of Tourism Research, Vol. 22 No. 1, pp. 1-20, doi: 10.1080/10941665.2016.1175488 (accessed 3 Januray 2022).
Mangold, W.G. and Faulds, D.J. (2009), “Social media: the new hybrid element of the promotion mix”, Business Horizons, Vol. 52 No. 4, pp. 357-365, doi: 10.1016/j.bushor.2009.03.002 (accessed 29 Januray 2022).
Mariani, M.M., Di Felice, M. and Mura, M. (2016), “Facebook as a destination marketing tool: evidence from Italian regional destination management organizations”, Tourism Management, Vol. 54, pp. 321-343, doi: 10.1016/j.tourman.2015.12.008 (accessed 7 March 2022).
Mason, M.C. and Paggiaro, A. (2012), “Investigating the role of festivalscape in culinary tourism: the case of food and wine events”, Tourism Management, Vol. 33 No. 6, pp. 1329-1336, doi: 10.1016/j.tourman.2011.12.016 (accessed 10 Januray 2022).
Miles, J. (2014), Instagram Power: Build Your Brand and Reach More Customers with the Power of Pictures, McGraw-Hill Education, New York.
Moe, W.W. and Trusov, M. (2011), “The value of social dynamics in online product ratings forums”, Journal of Marketing Research, Vol. 48 No. 3, pp. 444-456, doi: 10.1509/jmkr.48.3.444 (accessed 13 March 2022).
O'Reilly, T. (2005), “What is Web 2.0: design patterns and business models for the next generation of software”, Communications and Strategies, Vol. 1, p. 17.
Obar, J.A. and Wildman, S. (2015), “Social media definition and the governance challenge: an introduction to the special issue”, Telecommunications Policy, Vol. 39 No. 9, pp. 745-750, doi: 10.1016/j.telpol.2015.07.014 (accessed 24 Januray 2022).
Prahalad, C.K. and Ramaswamy, V. (2004), “Co-creation experiences: the next practice in value creation”, Journal of Interactive Marketing, Vol. 18 No. 3, pp. 5-14.
Qu, H., Kim, L. and Im, H.H. (2011), “A model of destination branding: integrating the concepts of the branding and destination image”, Tourism Management, Vol. 32 No. 3, pp. 465-476, doi: 10.1016/j.tourman.2010.03.014 (accessed 4 February 2022).
Sabate, F., Berbegal-Mirabent, J., Cañabate, A. and Lebherz, P.R. (2014), “Factors influencing popularity of branded content in Facebook fan pages”, European Management Journal, Vol. 32 No. 6, pp. 1001-1011, doi: 10.1016/j.emj.2014.05.001 (accessed 11 Januray 2022).
Saboo, S.B., Kumar, V. and Ramani, G. (2016), “Evaluating the impact of social media activities on human brand sales”, International Journal of Research in Marketing, Vol. 33 No. 3, pp. 524-541, doi: 10.1016/j.ijresmar.2015.02.007 (accessed 10 February 2022).
Sevin, H.E. (2014), “Understanding cities through city brands: city branding as a social and semantic network”, Cities, Vol. 38, pp. 47-56.
Silkes, C.A., Cai, L.A. and Lehto, X.Y. (2013), “Marketing to the culinary tourist”, Journal of Travel and Tourism Marketing, Vol. 30 No. 4, pp. 335-349.
Subrahmanyam, K., Reich, S.M., Waechter, N. and Espinoza, G. (2008), “Online and offline social networks: use of social networking sites by emerging adults”, Journal of Applied Developmental Psychology, Vol. 29 No. 6, pp. 420-433, doi: 10.1016/j.appdev.2008.07.003 (accessed 5 February 2022).
Swani, K., Milne, G.R., Brown, B.P., Assaf, A.G. and Donthu, N. (2017), “What messages to post? Evaluating the popularity of social media communications in business versus consumer markets”, Industrial Marketing Management, Vol. 62, pp. 77-87, doi: 10.1016/j.indmarman.2016.07.006 (accessed 2 February 2022). (accessed 5 Januray 2022).
Taylor, D.G., Lewin, J.E. and Strutton, D. (2011), “Friends, fans, and followers: do ads work on social networks?”, Journal of Advertising Research, Vol. 51 No. 1, pp. 258-275, doi: 10.2501/JAR-51-1-258-275 (accessed 5 Januray 2022).
Virtanen, H., Björk, P. and Sjöström, E. (2017), “Follow for follow: marketing of a start-up company on Instagram”, Journal of Small Business and Enterprise Development, Vol. 24 No. 3, pp. 468-484, doi: 10.1108/JSBED-12-2016-0202 (accessed 4 Januray 2022).
Vukolić, D. (2020), “Gastronomski proizvodi u funkciji razvoja različitih oblika turizma u Sremskom okrugu”, Održivi Razvoj, Vol. 2 No. 2, pp. 41-54, available at: https://scindeks-clanci.ceon.rs/data/pdf/2683-3654/2020/2683-36542002041V.pdf (accessed 30 Januray 2022).
Vukolić, D., Gajić, T. and Bugarčić, J. (2021a), “Stavovi lokalnog stanovništva o razvoju turizma u Sremskoj Mitrovici”, Trendovi U Poslovanju, Zbornik Radova Pete Nacionalne Naučno Stručne Konferencije Sa Međunarodnim Učešćem, Trendovi U Poslovanju, Visoka poslovna skola prof. dr Radomir Bojkovic, Krusevac, Serbia, pp. 229-309.
Vukolić, D., Gajić, T. and Bugarčić, J. (2021b), “Zadovoljstvo gostiju ponudom evropskih internacionalnih jela i proizvoda u restoranima Srbije”, Turistčko Poslovanje, Vol. 28, pp. 27-38, doi: 10.5937/turpos0-33646 (accessed 1 February 2022).
Vukolić, D., Gajić, T. and Gamor, E. (2021c), “The importance of tourist's perception of restaurant's offer in Banja Luka”, The Eight International Academic Conference, Banja Luka, pp. 401-411, doi: 10.7251/ZUPS2108401V.
Wally, E. and Koshy, S. (2014), “The use of Instagram as a marketing tool by Emirati female entrepreneurs: an exploratory study”, 29th International Business Research Conference, pp. 1-19.
Yu, C.E. and Sun, R. (2019), “The role of Instagram in the UNESCO's creative city of gastronomy: a case study of Macau”, Tourism Management, Vol. 75, pp. 257-268, doi: 10.1016/j.tourman.2019.05.011 (accessed 21 Januray 2022).
Yuan, Y., Chan, C.S., Eichelberger, S., Ma, H. and Pikkemaat, B. (2022), “The effect of social media on travel planning process by Chinese tourists: the way forward to tourism futures”, Journal of Tourism Futures, pp. 1-20, ISSN 2055-5911. doi: 10.1108/JTF-04-2021-0094 (accessed 16 Januray 2022).
Zadeh, A.H. and Sharda, R. (2014), “Modeling brand post popularity dynamics in online social networks”, Decision Suppo VRT Systems, Vol. 65, pp. 59-68, doi: 10.1016/j.dss.2014.05.003 (accessed 16 Januray 2022).
Zhou, L. and Wang, T. (2014), “Social media: a new vehicle for city marketing in China”, Cities, Vol. 37, pp. 27-32, doi: 10.1016/j.cities.2013.11.006 (accessed 15 Januray 2022).