The Role of Semiotics in Tourism Destination Branding through Social Media: The Case of Switzerland

 

ANTOINE GOLINVAUX

Project Assistant, MCI Group

IOANNIS EVAGELOU

Lecturer in Tourism & Postgraduate Programmes Leader, ΙΜΙ International Management Institute Switzerland

 

 

 

ABSTRACT

This paper investigates the use of semiotics in the branding of Switzerland as a tourism destination through social media, and specifically via Facebook. Destination branding has assumed an increasing importance over the past few years, and social media and semiotics have facilitated its rise. Semiotics corresponds to visible signs that, in this context, may be deployed as a means of attracting a customer’s attention and its effective utilisation can make a difference to a destination’s choice. Moreover, the branding of a destination may be effected through social media. It is a relatively cost-effective way to promote a destination and attempt to attract visitors. A conceptual framework was developed and content analysis was applied to 200 images uploaded by Switzerland Tourism on its Facebook page (DMO-uploaded and user-generated content) in order to identify the recurrent categories of attributes visible in imagery propagated. The findings indicated that around half the images had Nature & Landscape, Architecture/Buildings and People attributes. The investigation also highlighted that 22 Swiss cantons out of 26 were represented in the sample and that nearly half of the images were captured in Bern, Graubünden and Valais. Finally, the investigation provided four recommendations to Switzerland Tourism to improve its destination branding strategy on social media by using semiotics, such as to continue to upload images containing thematic concerns of escape, freedom and authenticity and to include their logo and slogan in the images. The research also pointed out interesting areas for further research.

Key Words: tourism destination, branding, semiotics, social media, Switzerland

 

 

INTRODUCTION

Over the past six decades, the tourism industry has grown exponentially and diversified, becoming one of the world’s most dynamic and rapidly expanding business sectors. The World Tourism Organization reported that world export earnings generated by the tourism industry amounted to USD 1.5 trillion, a record amount (UNWTO 2015b). It also represents nine percent of global GDP, and accounts for one in eleven jobs (UNWTO 2015c).

Due to the extraordinary growth of the industry, and to globalisation, the competition between tourism destinations has correspondingly increased. Kiralova and Pavliceka argued that destinations are “territories, geographical areas, such as a country, an island or town”, where “people travel and where they choose to stay for a certain period” and where “a combination of all products, services and experiences are provided locally” (Kiralova and Pavliceka 2015, p359). Destination Marketing Organisations (DMOs) have consequently grown in importance, their goal being to attract more visitors to their destination, and to generate more income. Every destination has a unique heritage, sites or culture (Morgan and Pritchard 2004), and DMOs have attempted to exploit these assets through engaging in new promotion strategies such as tourism destination branding. Through such differentiation, the DMOs hope to achieve a competitive advantage in one of the most fiercely competitive sectors of the global economy. They aim to establish a unique brand identity and brand image to provide visitors with positive initial impressions of the destination, and hopefully, to ultimately influence their holiday destination choice.

To brand and promote destinations, DMOs create advertising campaigns, which include destination logos, short messages, images, specific colours, sounds or more generally, any kind of signs. These signs, analysable through semiotic theory, attempt to capture the attention of potential visitors through engaging with the observer’s ability to recognise and receive such patterns, codes and sub textual information (Chandler 2007). These campaigns, if competently executed, aim to stimulate the development of the destination and brand awareness, increase the latter’s brand recognition, influence visitor perceptions, and develop positive associations with the brand/destination (Oswald 2007). Another strategy commonly used nowadays is the promotion of destinations through social media platforms, such as Facebook, Instagram or TripAdvisor, which allow individual users and DMOs to communicate, interact and exchange information with each other. This represents a valuable marketing tool for DMOs because they can easily share messages, videos and images of their destinations to market destinations to potential visitors.

This research focuses on Switzerland as a tourism destination. The country is mainly characterised by its desirable location and climate and abundant and visually appealing natural resources. The research was undertaken with the object of analysing the use of semiotics in the branding of Switzerland through the Facebook page of its DMO, Switzerland Tourism. Content analysis was developed and conducted by analysing 200 images collected through a random sampling on the Facebook page of Switzerland Tourism over the course of 2015. The aim of this was to identify the main categories of attributes observed in the uploaded images, which were used to brand the destination.

 

 

LITERATURE REVIEW

 

Branding

Branding is considered as a key element in the marketing strategies of organisations because it provides them valuable features and allows them to obtain a competitive advantage (Lynch and de Chernatony 2004). The American Marketing Association defines a brand as a "name, term, design, symbol, or any other feature that identifies one seller's good or service as distinct from those of other sellers” (American Marketing Association 2015). However, as Jevons stated (2005, p117), this definition is not service oriented and does not include “intangible components or consumer perceptions”. The definition provided by the European Brands Association can consequently complement the American definition: “a brand is the sum of [the consumer] knowledge and understanding of a product, service or company, and provides the means for exercising choice and preference. Over time, a product or service may develop in an individual's mind to become familiar, recognisable, reassuring, unique and trust inspiring - in other words, a strong brand” (European Brands Association 2015). The analysis of this second definition shows that a brand is not only characterised by a symbol or a logo. A brand aims to share emotional values with its customers by communicating to them about itself, about its culture and its products or services (European Brands Association 2015; Lynch and de Chernatony 2004; Dinnie 2008).

 

Destination Branding

The degree of interest shown by tourists in the end-destination of their trip(s) has increased in recent years, accompanied by ever-increasing levels of competitiveness within the travel industry (Baker and Cameron 2008; Blain, Levy and Ritchie 2005; Buhalis 2000). The purpose of Destination Marketing Organisations (DMOs) is to attract both more visitors and investors to certain global travel destinations, but also to increase awareness about the destinations. Destinations are open to promotion and branding in much the same way as products and services, with the objective of “mak(ing) people aware of the location and then link(ing) desirable associations to create a favourable image to entice visits and businesses” (Baker and Cameron 2008, p86).

Blain, Levy and Ritchie define destination branding as the “marketing activities that (1) support the creation of a name, symbol, logo, word mark or other graphic that readily identifies and differentiates a destination; that (2) consistently convey the expectation of a memorable travel experience that is uniquely associated with the destination; that (3) serve to consolidate and reinforce the emotional connection between the visitor and the destination; and that (4) reduce consumer search costs and perceived risk. Collectively, these activities serve to create a destination image that positively influences consumer destination choice” (Blain, Levy and Ritchie 2005, p337).

Finally, social media plays an undeniably powerful role in destination branding and it constitutes an important strategy for the DMOs (Kiralova and Pavliceka 2015). DMOs should create their own online community to engage visitors, encourage interactions (between visitors and potential visitors or between visitors and DMOs for example) and share with them experiences and interesting/relevant content.  the benefits of social media for tourism destination branding are numerous and “can be summed up as follows: (1) (favourable) ROI; (2) increase of the number of visitors; (3) increase of positive awareness; (4) increase of destination preference; (5) awards; (6) publicity; (7) rise of website hits; (8) increase of number of website and Facebook referrals; (9) increase of number of Facebook fans; (10) user generated content; (11) acquisition of new ambassadors for the destination; (12) public relations” (Kiraľova and Pavličeka 2015, p363).

 

Semiotics

Semiotics, also called semiology, is the study and the science of signs (Berger 2014; Tresidder and Hirst 2012; Chandler 2007; Oswald 2012). Differing definitions of semiotics exist. Mick, for example, in 1986 stated that signs are understood and simply represented by “anything that stands for something (its object), to somebody (interpreter), in some respect (its context)” (Mick 1986, p198). Eco offered an alternative definition: “semiotics is concerned with everything that can be taken as a sign” (Eco 1976, p7 in Chandler no date). Finally, semiotics can also be defined as “the study of signs and systems of representation” (Tresidder and Hirst 2012, p153).

Therefore, everything represented by words, language, images, actions or objects are considered as signs (Chandler 2007, Echtner 1999). Nevertheless, these have no intrinsic meaning (Chandler 2007) unless we endow them with one; once this occurs, they become signs. In addition, their interpretation is also contingent on the perceptions of the observer: this may differ from one person to another, depending on a broad range of factors including culture, market segmentation or lifestyle (Tresidder and Hirst 2012).

Although semiotics is not a recent discipline, Chandler states that no “widely agreed theoretical assumptions, models or empirical methodologies” (Chandler 2007, p4) have yet been developed to study signs. He adds that “semiotics has tended to be largely theoretical” and “many of its theorists are seeking to establish its scope and general principles” (Chandler 2007, p4). Current research tries to categorise the codes and signs to provide a better understanding of semiotics, but this is proving to be a complex endeavour, as semiotics includes elements of linguistics, philosophy, psychology, anthropology, sociology, aesthetics, etc. (Echtner 1999; Chandler 2007; Oswald 2012).

Two authors are acclaimed to be the “fathers” of semiotic analysis: the Swiss linguist Ferdinand de Saussure and the American philosopher Charles Sanders Peirce (Chandler 2007; Tresidder and Hirst 2012). They developed two dominant contemporary and theoretical models that remain helpful tools to analyse signs and symbols. Roland Barthes utilised and expanded on Saussure’s model to determine that meaning has different levels.

At the end of the nineteenth century, Ferdinand de Saussure stated in his Course in General Linguistics that semiology is a science “which studies the role of signs as part of social life” (Chandler 2007, p2). He formulated a binary model to define the linguistic signs, which are composed of the signified and the signifier (Oswald 2012; Chandler 2007). The model is summarized in the Figure 1 below. On one hand, the signifier is a material or physical form of the sign. It means that the signifier “is something which can be seen, heard, touched, smelled or tasted” (Chandler 2007, p15). It is also called the “sound pattern” (Chandler 2007, p14). On the other hand, the signified is the concept that the signifier tends to represent in the mind of people (a mental construct, a notion). The sign is obtained thanks to the association of the two elements of the Saussure’s model. This relationship between the signified and the signifier is termed the signification, and is represented by the two arrows in Figure 1. The signification and the meaning of the sign can change depending on the context and the person as the relation is “arbitrary and based on convention” (Berger 2014, p22). The role of social code or the culture of the observers also impact the meaning of the sign. Finally, this model is useful to analyse brand logos or symbols (Oswald 2012).

 

 

Figure 1: Representation of Saussure’s model (Source: Based on Saussure 1967, p.158)

 

At the same time as Saussure, Charles Sanders Peirce developed an alternative model of the sign. According to Peirce, people think only through signs and he defines signs as a combination of three elements (Stanford Encyclopaedia of Philosophy 2010). Firstly, the representatem is understood as the form (materiality or immateriality) taken by the sign because it represents something (Chandler 2007). In Saussure’s model, it corresponds to the signifier. Secondly, the representatem enters in relation with the object, which corresponds to a representation beyond the sign (also called the referent). The inclusion of the object illustrates the main difference between this model and the model of Saussure. Thirdly, the interpretant represents the “sense made by the sign” (Dahlstrom and Somayaji 2003; Chandler 2007, p29) and the interpretation of the sign that people have in their mind. A parallel with the signified in Saussure’s model can be drawn. In addition, all three elements interact with each other in a triangle (Echtner 1999) and, in the words of Chandler, the sign is “a unity of what is represented (the object), how it is represented (the representatem) and how it is interpreted (the interpretant)” (Chandler 2007, p29). The triangle is shown in the following figure. The relation between the representatem and the object is a broken line because there is no clear “observable and direct relationship” between the two elements (Chandler 2007, p30).

Figure 2: Combination of the three elements of the sign (Dahlstrom and Somayaji 2003)

 

Based on the model of Pierce, Echtner (1999) adapted the semiotic triangle to tourism destinations. The representatem (designatum) corresponds to the tourism destination. Then, advertisements are created to promote the destination and to transfer meanings to the customers. They include signs, logos, images or text, for instance. The advertisements correspond therefore to the object (sign), and the visitors to the interpretants. Figure 3 illustrates the semiotic triangle for the tourism industry.

Figure 3: The semiotic triangle for the tourism industry (Echtner 1999, p53)

 

 

METHODOLOGY

The purpose of this section is to present and discuss the research methodology, principally content analysis. It attempts to analyse and identify which categories of semiotics and attributes are used in images posted by Switzerland Tourism on Facebook to promote Switzerland as a tourism destination. In addition, this chapter includes an analysis of the methods used in the collection of primary data, as well as the procedure followed to develop the attributes’ categories and to code the data.

 

Content analysis

Content analysis is generally used to analyse textual materials, but it can also be utilised as a research technique to study the characteristics of advertisements and images, because it “aims at describing, with optimum objectivity, precision, and generality, what is said on a given subject in a given place at a given time” (Lasswell, Lerner, and Pool 1952, cited in Stepchenkova, Kirilenko, and Morrison 2009, p455). Berelson defined content analysis as ‘‘a research technique for the objective, systematic, and quantitative description of the manifest content of communication’’ (Berelson 1971, cited in Anderson, Dewhirst, and Ling 2006, p257). Similarly, Kerlinger (1986, in Binsbergen, 2013) articulates content analysis as "a method of studying and analysing communication in a systematic, objective, and quantitative manner for measuring variables".

In the abovementioned definitions, the three italicised words express the key elements of content analysis (Anderson, Dewhirst, and Ling 2006). Firstly, objectivity is the “avoidance of (conscious) bias and subjective selection during the conduct and reporting of research” (Saunders, Lewis and Thornhill 2012, p676). This means that the data collection must be structured, consistent and adhere to strict rules. In the case of content analysis, a list of attributes should be defined before collecting the data to construct the basis of the investigation. The objectivity of the researcher(s) is important as it has the potential to affect the quality of the research (Saunders, Lewis and Thornhill 2012). When coding the results in the case of several researchers, they “should secure highly replicable and reproducible results and arrive at similar conclusions” (Anderson, Dewhirst, and Ling 2006, p257). This serves to avoid misrepresentations in data collection and the risk of errors in the findings. Secondly, as “scientific problems or hypotheses are examined” (Anderson, Dewhirst, and Ling 2006, p257), systematisation is necessary to ensure consistency, especially in the procedures to follow when coding the data, and in the random sample selection. The results should be generalizable. Thirdly, quantification means that data is correctly coded into different pre-defined categories to attain statistically accurate results. The numerical results from the sample can be generalised to the whole population (of images or texts) and thus the researcher may “derive patterns in the analysis and reporting of information” (Vitouladiti 2014, p279). Content analysis can also be qualitative, but its objective is more exploratory and does not include statistics (Vitouladiti 2014; Stepchenkova, Kirilenko, and Morrison 2009).

Based on Hsieh and Shannon (2005, p.1285), content analysis ideally follows a seven-step process, the aim of which consists in “formulating the research questions to be answered, select the sample to be analysed, defining the categories to be applied, outlining the coding process and the coder training, implement the coding process, determining trustworthiness, and analysing the results of the coding process”. In regards to the categories’ definition, the authors stated that three different approaches can be applied: categories can be derived either directly from the data, or from previous research on the same research topic and then applied to the current study (categories can also be added), or from the counting of attributes, leading to comparisons (Hsieh and Shannon 2005). The research in this paper builds its investigation on the attribute categories developed in previous research, and they constitute the basis for coding the data. This process is arguably more structured than the two other techniques (Hsieh and Shannon 2005).

 

Data Collection and Selected Sample

To conduct the content analysis, images of Switzerland were downloaded from the Facebook page of Switzerland Tourism (www.facebook.com/MySwitzerland). Overall, the DMO posted more than 13,600 images on its Facebook page between 2013 and the end of 2015, in different albums. However, due to the high volume of data the research decided to focus on the most recent of these images. Over the past year, 551 images were uploaded between 01 January and 31 December 2015. The data were collected over one day and all the images were downloaded twice. They were coded 0001 to 0551. This double process ensured that no image was omitted. After saving them in chronological order, the images were analysed sequentially to identify whether each one was a DMO or a user-generated ‘fan’ image, and to note the corresponding location where the image was captured. That information was subsequently inputted into an Excel spreadsheet. The images that did not have a location written in their description were excluded from the sample, as well as other images that were considered as irrelevant (for instance, the image of the Instagram logo that encourage people to follow the DMO on the eponymous social network), or because they were posted twice on different days. The final number of images in the sample was 384: 245 images uploaded by the DMO and 139 images pictured by ‘fans’, but uploaded by the DMO with the hashtag #fanphoto or #SwissSelfie in their description.

 

Category Development and Data Coding

Before the coding of the data, a development of categories is required. As Hsieh and Shannon (2005, p1285) state, categories are “patterns or themes that are directly expressed in the text or are derived from them through analysis”. This definition is also applicable for images. As mentioned earlier, the categories used for this research are derived from existing literature. The research mainly follows the study conducted by Stepchenkova and Zhan (2013). Based on the research of Echtner and Ritchie (1993) and Albers and James (1988), Stepchenkova and Zhan highlighted 20 categories that represent tourism images of Peru (Stepchenkova and Zhan 2013). Those categories were used by the researchers as a basis for this investigation and include, for example: “Nature & Landscape”, “Wildlife”, “Leisure activities” and “Country landscape”. Three new categories were added by the principal researcher after seeing all the images once: “Swiss flag”, “Sport activities” and “Wellness”. In addition, the category “Domesticated animals” was renamed “Animals” to include all the animals that were observed in the sample images. Finally, as Switzerland has virtually no archaeological sites, this category was removed. The final list is composed of 22 categories of attributes.

 

 

RESULTS & DISCUSSION

The aim of this analysis is to determine which categories of attributes are found in the DMO and ‘fan’ images uploaded by Switzerland Tourism on its Facebook page to brand Switzerland as a tourism destination. The categories of attributes are based on a study undertaken by Stepchenkova and Zhan (2013). However, the list used for this research was modified, with three categories added, one removed and one renamed. As mentioned in the methodology, a total of 200 images were content-analysed by two researchers.

Table 2 and bar chart (Figure 4) below summarize the share, expressed as a percentage, represented by each category of attributes that were identified in the sample, with DMO and ‘fan’ images undifferentiated. The categories are ordered by their degree of frequency in the images sample.

 

 

Table 2: Share (in %) represented by each category of attributes in the images sample (in share order)

 

As shown in the previous table and chart, the first category, Nature & Landscape, has the biggest share of the images sample. 120 images were classified under this category, representing a 60% share of the images. However, there is an important discrepancy between the two samples under analysis, as this category is represented in 80% of ‘fan’ images in comparison to only 40% of DMO images.  Architecture/Buildings is the second largest category in the sample with a 44% share of the total images surveyed. However, only 27 user-generated ‘fan’ images were classified in that category compared to 61 DMO images.  Thirdly, People were identified in 69 images out of 200 (34.5% of the sample). This category includes local people, tourists, adventurers, skiers who were visible in the images and images containing Sebi and Paul - two iconic Swiss characters representing Switzerland in the promotional material of Switzerland Tourism. More human faces were observed in DMO images; 43%, as opposed to 26% of ‘fan’ images.

In the overall sample of 200 images, 22 cantons are represented. Each canton had a share of between 1% and 18.17% of all images, and the represented cantons are shown on average 9.1 times in the sample (4.55%). Bern is ranked first, being the most represented canton in the sample, followed by Graubünden and Valais.

In the DMO sample, the images were distributed between 19 cantons of Switzerland. The representation of each canton varied between 1.5% to 19% of all photos, with an average of 5.26%. Graubünden and Bern were the most represented cantons in the DMO sample, with 19% and 15.50% respectively, followed by Ticino, Zürich, Vaud and Valais.

Figure 4: Share (%) represented by each category of attributes in the images sample (in share order)