GERMAN STUDENT’S PERCEPTIONS OF BULGARIA AS A TOURIST DESTINATION – A MOLECULAR APPROACH TO DESTINATION IMAGE ASSESSMENT Stanislav Ivanov, Ph. D. International University College 3 Bulgaria Str., 9300 Dobrich, Bulgaria Email:
[email protected] Abstract: The paper presents the results of a study of the image of Bulgaria as a tourist destination among a group of German students visiting Bulgaria for a first time. Similarly to Ivanov and Illum (2010) the paper adopts the Lederer and Hill (2001) and Silver and Hill (2002) molecular approach to branding and combines it with the John et al (2006) brand concept mapping technique. Respondents prepared individual concept maps of Bulgaria as a tourist destination which were consequently aggregated to derive a group consensus cognitive map. Results show that respondents have very narrow perceptions about Bulgaria as a tourist destination. Managerial implications of the study are also discussed. Key words: Bulgaria, destination marketing, concept maps, perceptions, destination brand molecule
1 Electronic copy available at: http://ssrn.com/abstract=1603494
GERMAN STUDENT’S PERCEPTIONS OF BULGARIA AS A TOURIST DESTINATION – A MOLECULAR APPROACH TO DESTINATION IMAGE ASSESSMENT Introduction Destination image, defined as the compilation of beliefs and impressions based on information processing from various sources over time (Crompton, 1979; Yüksel and Akgül, 2007), and its measurement have long been on the research agenda (Gallarza, Gil and Calderon, 2002; Gartner, 1989, 1993; Nadeau, Heslop, O’Reilly, Luk, 2008; Pike, 2002; Rakadjiyska, 2002; Telisman-Kosuta, 1989; White, 2004). This is not surprising because by creating a favorable image a destination will attract tourists and achieve profitability (Echtner and Ritchie, 1991; Phelps, 1986). Existing methodologies on destination image measurement (Gallarza, Gil and Calderon, 2002) try to reconcile the individual perceptions and develop an aggregate picture of destination image but they suffer from different pitfalls (Ivanov and Illum, 2010). Nonquantitative methods like free elicitation, focus groups, in-depth interviews, content analysis (Choi, Lehto, Morrison, 2007; Hankinson, 2004; Prebensen, 2007), provide rich data, that allow very subtle nuances in a destination’s image to be captured, but information aggregation is often subject to a researcher’s discretion. They require a lot of time to implement and data comparability over time and space may be difficult to achieve. Quantitative methods (Baloglu and McCleary, 1999; Beerli and Martin, 2004; Chen, 2001; Correia, Oliveira, Silva, 2009; Gartner, 1989; Son and Pearce, 2005) provide comparable data in a standardized form but the use of preformulated questionnaires to assess the destination image distorts the primary data because respondents are reminded about specific attributes of the destination and are fostered to give an answer. As each method has its own advantage often they are used simultaneously, complementing each other (e.g. Govers, Go, Kumar, 2007; Hunter and Suh, 2007; Luque-Martinez, Del Barrio-Garcia, Ibanez-Zapata, Molina, 2007). Current paper aims at addressing these pitfalls. It goes beyond the above mentioned methodologies and adopts a relatively new instrument in destination image measurement – the destination brand molecule. It has been developed by Silver and Hill (2002) as a tool to identify potential opportunities for rebranding the USA. It is based on the Lederer and Hill
2 Electronic copy available at: http://ssrn.com/abstract=1603494
(2001) concept of the brand portfolio molecule. The latter is presented as a set of interconnected atoms, representing individual brands included in company’s portfolio. In a molecule map, individual brands take the form of atoms clustered in ways to reflect how customers see them (Lederer and Hill, 2001: 126). Each connection between brand atoms in a portfolio molecule might exert positive, neutral or negative impact on a customer’s purchase decision. The strongest point in the Lederer and Hill (2001) approach is that it relies on customer perceptions about relationships between brands in the brand portfolio and shows that brands are not perceived by customers in isolation but in their integrity with other strategic or support brands in a company’s portfolio. The main problem with the Lederer and Hill (2001) and the Silver and Hill (2002) papers is that authors do not elaborate the methodology for developing the molecules. They do not explain in details how the associations were derived and ranked, or how the strength of the links between the associations was determined. In this regard, similar to Ivanov and Illum (2010), current paper combines the destination brand molecule with the brand concept mapping technique (John et al, 2006; Hui, Huang and George, 2008; Martínez and Martínez, 2009). Brand concept maps are used to examine customer perceptions toward and associations with an existing brand and have been successfully applied to destination image measurement by Ivanov and Illum (2010). Methodology The destination brand molecule of Bulgaria was created by adopting the methodology developed by John et al. (2006) for the brand concept map for the Mayo Clinic and applied for Las Vegas by Ivanov and Illum (2010). The study took place in April 2009. Twentytwo students from Germany and their 2 lecturers visiting Bulgaria for the first time were asked to participate in the study. Two of the students were from Bulgaria and thus were excluded from the survey. Finally the cohort included 22 respondents. The research methodology included the following five phases: Phase 1.: Elicitation – identification of possible associations to be potentially included in a molecule. →Step 1.1.: Preparation of individual lists of associations by the respondents. The 22 respondents were asked to prepare their own individual and anonymous lists of associations with the destination brand ―Bulgaria‖ and were allowed about 10 minutes to
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complete this procedure. →Step 1.2.: Preparation of the aggregated list of associations. Respondents’ lists were merged and the frequencies of mentioning of each association calculated. Descriptive statistics for the association lists are presented in Table 1. Respondents identified 64 different associations with the brand ―Bulgaria‖ which were mentioned 140 times, an average of 2.19 times per association. The average length of an association list was 6.36 which is considered too short (for comparison Ivanov and Illum (2010) report for one of the surveyed cohorts an average length of an association list to be 15 entries). The full list of mentioned associations and their respective frequencies are presented in Table 2. ============== Insert Table 1 here ============== Insert Table 2 here ============== →Step 1.3.: Selection of association lists to be used in the next phase of the research – mapping: John et al. (2006: 552) suggest that only those associations mentioned in at least 50% of the individual lists should be selected for the next stages of brand concept map construction. In current survey we followed Ivanov and Illum (2010) notion that using only the 50%+ associations would artificially limit the number of associations in the concept maps and thus we selected for the next phase of the research the associations mentioned by at least 18-20% of respondents. The final list includes only 10 associations (see entries in italics in Table 2). Phase 2.: Mapping – preparation of individual brand molecules for Bulgaria by the respondents using the association lists from Step 1.3. Respondents were asked to prepare individual brand molecules of Bulgaria and to apply the following mapping rules: ● use only the associations from Step 1.3. Respondents were not required to include all associations from this list in the molecule they created. ● use 1, 2 or 3 lines between associations to denote a weak, medium or strong connection between the associations, respectively. ● use +, – or 0 to denote a positive, negative or neutral influence of a particular association
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to the overall image of the destination – a so called ―valence‖ of an association. The 22 respondents generated 21 molecules that followed the above procedures and could be used for the research. Phase 3.: Aggregation – coding and aggregating the individual molecules. We next calculated the statistics shown in Table 3 and aggregated the data from the individual brand molecules in Table 4. ============== Insert Table 3 here ============== Insert Table 4 here ============== Phase 4.: Consensus molecule – combining the individual molecules into one consensus molecule. →Step 4.1.: Selection of first-order associations – the i-th association is considered to be of first order if it simultaneously fulfils the following conditions: ● R1i
M - in more than one-half of the individual molecules collected, the i-th 2
association is mentioned as a first-order association ● Ci
C - the i-th association has a higher than the average total number of connections
with other associations ● Ci
C - the i-th association has an average number of connections with other
associations in one molecule higher than the total average number for all associations in all collected molecules The combination of the three conditions means that the i-th association is central (core) to the destination brand. →Step 4.2.: Selection of second-order associations – the i-th association is considered to be of second-order if it fulfils either of the following two sets of conditions below: Condition Set I: The i-th association fulfils simultaneously the three conditions below: ● R2i
M - in more than one-half of the individual molecules the i-th association is 2
mentioned as a second-order association ● Ci
C - the i-th association has a higher than the average total number of connections
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with other associations ● Ci
C - the i-th association has an average number of connections with other
associations in one molecule higher than the total average number for all associations in all collected molecules Condition Set II: ● in more than one-half of the individual molecules the i-th association is linked with a first-order association selected in previous Step 4.1. Analogically, we derived the third- and higher-order associations. →Step 4.3.: Determination of association connections – only those mentioned by at least 25% from the respondents were selected for inclusion in the consensus brand molecule. →Step 4.4.: Determination of the strength of connection between two associations: ● weak – Lij
1;1.5
● medium –- Lij ● strong – Lij
1.5;2.5
2.5;3
→Step 4.5.: Determination of the valence of an association in the consensus molecule: ● positive – V i ● neutral – V i ● negative – V i
0.5;1 0.5;0.5
1; 0.5
→Step 4.6.: Use of suitable colors and dashing to show the different associations, their valences and the strength of connections between them. The final result of Phase 4 was the consensus brand molecule of Bulgaria, generated by the responses of the German students. It should be noted that this molecule represents the predominant views of the respondents, not the perceptions of a single person. Phase 5.: Validity analysis – a check to determine whether the aggregations performed are methodologically correct. Following John et al. (2006) and Ivanov and Illum (2010) a random half-split of the individual molecules was performed. A new consensus molecule was derived (named ―validation consensus molecule‖) and compared the associations included in it with the associations in the original consensus molecule. Results
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The consensus destination brand molecule of Bulgaria is presented in Figures 1. =============== Insert Figure 1 here =============== All associations with the brand ―Bulgaria‖ are considered of being of first order, because more than 50% of respondents mentioned a direct link between them and the core item ―Bulgaria‖. However, the strength of the links is not equal. The strongest association link with ―Bulgaria‖ is ―Sunny beach‖ (L=2.35), while the weakest is ―rich/poor‖ (L=1.47). It is interesting to note that neither respondents depicted any association as third or higher order – all of them were shown as either first or second order associations. There seam to be 3 clusters with not very complex links among the associations included in them: 1. The first one includes the sea-side resorts ―Sunny beach‖ and ―Golden sands‖, ―Black sea‖ and ―cheap‖. ―Black sea‖ has nearly the same strength of the association links with ―Sunny beach‖ (L=2.29) and ―Golden sands‖ (2.27). Both resort associations have connections with ―cheap‖, but these links were mentioned by less than 50% of respondents. ―Sunny beach‖ and ―Golden sands‖ are also connected in respondents’ minds (L=2.17). It should be emphasized that all 4 associations in this cluster have a positive valence, i.e. they contribute to the positive image of the destination. 2. A second cluster is related with political and economic issues of the destination. It includes associations that have predominantly neutral valence and have weaker connections with the brand ―Bulgaria‖ compared to the entries in the previous cluster – ―Eastern Europe‖ (V=0, L=1.81), ―ex-communist‖ (V=0, L=1.69), ―Transformation‖ (V=0, L=1.94), ―Rich/Poor‖ (V=-1, L=1.47) and ―Sofia‖ (V=0, L=2.22). The five associations in the cluster are also interconnected – being an Eastern European country, Bulgaria is perceived as ex-communist, that experiences transformation in economic, social and political aspects, which results in division between rich and poor strata of the society, especially visible in the capital Sofia. 3. ―Mountains‖ stays as a relatively isolated association that is linked only with the core brand (L=1.44) but exert a positive impact on the image of Bulgaria among respondents.
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To validate the aggregation, one-half of the individual molecules were randomly selected to create a new validation consensus brand molecule. It was found that all associations, connections between them from and valences the original brand molecule were replicated in the new validation molecule denoting that the original aggregation was performed correctly. Discussion and conclusion Study results show that respondent had too narrow view of Bulgaria as a tourist destination. Forty-seven out of 64 of the associations (74%) identified during the elicitation stage were mentioned by only 1 or 2 respondents while only one (the Black sea) was mentioned by more than half of them. The association lists were very short as well with an average length of 6.36 entries denoting the lack of information about the destination among respondents. Being first-time visitors they did not have any prior experience that could influence their perceptions but the latter were shaped only by the previous information about the country and perceptions formed during the first days of the visit to Bulgaria. Results are in line with Rakadjiyska (2002) conclusions about the tourists’ perceptions of destination Bulgaria – respondents in our survey show a positive attitude towards the tourist resources of the destination, but neutral or negative towards the political and economic development of Bulgaria. Looking at the consensus brand molecule of Bulgaria we can conclude that the tourism related associations (―Sunny beach‖, ―Golden sands‖, ―Mountains‖, ―Black sea‖), although having strong connections with the core brand ―Bulgaria‖, do not prevail in respondents’ perceptions about the destination. Therefore, government authorities responsible for the promotion of the destination should put greater emphasis on the provision of rich and abundant information about the destination, including the tourist resources, resorts, leisure activities, special events, etc. A greater presence of Bulgaria in the media in a positive light, participation in travel fairs (Ivanov and Webster, 2008), improved destination websites of the country as a whole and of different towns/regions/resorts in Bulgaria, a more focused branding of the destination would have a positive impact on potential tourists’ perceptions about Bulgaria and increased visitation. Only when potential tourists have positive perceptions about a destination they will include it into their consideration set when selecting a destination for their holidays.
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The research is not without limitations. The sample included only 22 students and lecturers visiting Bulgaria on an educational trip. This distorts the data on the basis of the demographic and educational characteristics of respondents. Further research should expand the sample to make it representative for all foreign tourists in Bulgaria. The influence of previous visits to Bulgaria on the tourists’ perceptions could also be examined. Acknowledgments: The author is grateful to Prof. Harald Pechlaner and his students from the Catholic University of Eichstaett-Ingolstadt, Germany, for taking part in the research. References: Baloglu, S., K. W. McCleary (1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868-897. Beerli, A., J. D. Martin (2004). Factors influencing destination image. Annals of Tourism Research, 31(3), 657–681. Chen, J. S. (2001). A case study of Korean outbound travelers' destination images by using correspondence analysis. Tourism Management, 22(4), 345-350. Choi, S. X. Y. Lehto, A. M. Morrison (2007). Destination image representation on the web: Content analysis of Macau travel related websites. Tourism Management, 28 (1), 118–129. Correia, A, N. Oliveira, F. Silva (2009). Bridging perceived destination image and market segmentation – An application to golf tourism. European Journal of Tourism Research, 2(1), 41-69. Crompton, J. L. (1979). An assessment of the image of Mexico as a vacation destination and the influence of geographical location upon that image. Journal of Travel Research, 17(4), 18-24. Echtner, C. M., Ritchie, J. R. B. (1991). The meaning and measurement of destination image. Journal of Tourism Studies, 2(2), pp. 2–12. Gallarza, M. G., I. Gil, H. Calderon (2002). Destination image. Towards a conceptual framework. Annals of Tourism Research, 29(1), 56-78. Gartner, W. (1989). Tourism Image: Attribute Measurement of State Tourism Products Using Multidimensional Techniques. Journal of Travel Research, 28(2), 16–20. Gartner, W. (1993). Image Formation Process. Journal of Travel and Tourism Marketing, 2(2/3), 191–215.
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Govers, R., F. M. Go, K. Kumar (2007). Virtual destination image. A new measurement approach. Annals of Tourism Research, 34(4), 977–997. Hankinson, G. (2004). The brand images of tourism destinations: A study of the saliency of organic images. Journal of Product & Brand Management, 13(1), 6-14. Hui, S. K., Y. Huang, E. I. George (2008). Model-based analysis of concept maps. Bayesian Analysis, 3(1), 1-34. Hunter, W. C., Y. K. Suh (2007). Multimethod research on destination image perception: Jeju standing stones. Tourism Management, 28(1), 130–139. Ivanov, S., S. F. Illum (2010) Destination Brand Molecule. Available at SSRN: http://ssrn.com/abstract=1586263 Ivanov, S., C. Webster (2008) Marketing the Bulgarian Tourism Product—the Economic Geography of Long-term and Short-term Investments. Paper presented at GEOTOUR 2008 Conference, 26-28 June 2008, Krakow, Poland. Available at SSRN: http://ssrn.com/abstract=1331064 John, D. R., B. Loken, K. Kim, A. B. Monga (2006). Brand concept maps: A methodology for identifying brand association networks. Journal of Marketing Research, 43(4), 549-563. Lederer, C., S. Hill (2001). See your brands through your customers’ eyes. Harvard Business Review, 79(6), 125-133. Luque-Martinez, T., S. Del Barrio-Garcia, J. Ibanez-Zapata, M. A. R. Molina (2007). Modeling a city’s image: The case of Granada. Cities, 24(5), 335–352. Martínez, J.A., L. Martínez (2009). La calidad percibida en servicios deportivos; mapas conceptuales de marca. Revista Internacional de Medicina y Ciencias de la Actividad Física y el Deporte, 9(35), 232-253. Nadeau, J., L. Heslop, N. O’Reilly, P. Luk (2008). Destination in a country image context. Annals of Tourism Research, 35(1), 84–106. Phelps, A. (1986). Holiday destination image – the problem of assessment. An example developed in Menorca. Tourism Management, 7(3), 168-180. Pike, S. (2002). Destination image analysis—a review of 142 papers from 1973 to 2000. Tourism Management, 23(5), 541–549. Prebensen, N. K. (2007). Exploring tourists’ images of a distant destination. Tourism Management, 28(3), 747–756. Rakadjiyska, S. (2002). The image of tourism-oriented Bulgaria as a competitive advantage. Proceedings of the Tourism Marketing Conference, 31st May – 2nd June
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2002, Grand hotel Varna. Silver, S., S. Hill (2002). Selling brand America. Journal of Business Strategy, 23(4), 1015. Son, A., P. Pearce (2005). Multi-faceted image assessment: International students’ views of Australia as a tourist destination. Journal of Travel & Tourism Marketing, 18(4), 21-35. Telisman-Kosuta, N. (1989). Tourism Destination Image. In S. F. Witt and L. Moutinho (Eds.), Tourism Marketing and Management Handbook (pp. 557–561). Cambridge: Prentice Hall. Yüksel, A., O. Akgül (2007). Postcards as affective image makers: An idle agent in destination marketing. Tourism Management, 28(3), 714–725. White, C. J. (2004). Destination image: to see or not to see? International Journal of Contemporary Hospitality Management, 16(5), 309-314.
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Table 1. Descriptive statistics in individual association lists Statistic Total number of associations Times all associations mentioned Average times one association mentioned Total number of association lists Average length of one association list
Value 64 140 2.19 22 6.36
Table 2. Aggregated association lists Association Bulgarian respondents (n=22) Black sea Sunny beach Sofia ex-communist cheap rich/poor mountains transformation Eastern Europe Golden Sands parties football (in 1990s) good food nature friendly people Orthodox USSR destroyed streets crimes 8 million inhabitants chaotic traffic thermal springs sun and beach Varna monasteries big hotels good wine hospitality European union beautiful landscape differences waste ruins agriculture warmer weather than Germany folklore
Times mentioned 12 9 8 5 5 4 4 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
Percent mentioned 54,55% 40,91% 36,36% 22,73% 22,73% 18,18% 18,18% 18,18% 18,18% 18,18% 13,64% 13,64% 13,64% 13,64% 13,64% 13,64% 13,64% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 9,09% 4,55% 4,55% 4,55% 4,55% 4,55% 4,55%
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Association Times mentioned Percent mentioned alcohol excesses 1 4,55% drunken student 1 4,55% vodka 1 4,55% Balkan 1 4,55% German products 1 4,55% poor cities 1 4,55% discos 1 4,55% bears 1 4,55% Balkan music 1 4,55% Bourgas 1 4,55% family holidays 1 4,55% big resorts 1 4,55% relaxed people 1 4,55% horses in city traffic 1 4,55% funny speech 1 4,55% less infrastructure 1 4,55% underweight girls 1 4,55% drinking tourism 1 4,55% dirty 1 4,55% shy people 1 4,55% ski 1 4,55% Cyrillic alphabet 1 4,55% Eurovision song contest 1 4,55% Balakov 1 4,55% not organized people 1 4,55% Balearics on the Balkans 1 4,55% summer destination 1 4,55% ancient 1 4,55% * Associations in Italics are included in the mapping process
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Table 3. Coding and aggregation statistics Statistic Symbol and calculation Total number of individual destination brand molecules М Total number of associations mentioned in individual N molecules Times i-th association mentioned in individual molecules Ni Times the connection between i-th and j-th associations Nij mentioned Strength of connection between the i-th and j-th associations in Lij a particular molecule Average strength of connection between the i-th and j-th Lij L ij associations in all molecules N ij Valence of i-th association Vi Average valence of i-th association in all molecules Vi i Vi Ni Number of connections of i-th association with other Ci N ij j associations Average number of connections of i-th association with other Ci Ci associations per one molecule Ni Average total number of connections of one association in all Ci i molecules C N Total average number of connections of one association per Ci i one molecule C Ni i
Number of first-order connections of an association – times the association mentioned in all molecules with a direct connection with Bulgaria Number of second-order connections of an association – times the association has connections with a first-rank association in all molecules Number of third- and higher-order connections of an association – times the association has connections with a second- or higher-order association in all molecules
R1i R2i R3i
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Table 4. Aggregated statistics from the individual brand molecules of Bulgaria
- - 17 11 16 18 17 16 16 16 16 15 20 16 2 2 1 17 3 0 17 0 1 1 14 0 0 0 7 6 46 20 0 1 19 -1 17 3 0 11 0 0 6 0 4 5 3 2 0 31 18 13 5 0 1 16 2 0 16 1 0 3 1 0 1 1 2 1 26 18 6 11 1 0 18 0 0 18 1 6 3 2 4 1 2 2 1 40 19 16 3 0 1 17 2 0 17 14 0 1 2 0 1 1 3 11 50 19 4 6 9 0 16 3 0 16 0 4 0 4 0 12 9 4 0 49 19 12 5 2 1 16 3 0 16 0 5 1 1 1 12 13 3 0 52 18 3 11 5 0 16 2 0 16 0 3 1 2 1 9 13 3 0 48 19 15 3 1 1 16 3 0 16 7 2 2 2 3 4 3 3 6 48 19 15 4 0 1 15 4 0 15 6 0 1 1 11 0 0 0 6 40 Total average number of connections of one association in all molecules 21,5 Total average number of connections of one association per one molecule 2,275
Average number of connections per one molecule
Total number of connections
Golden Sands
cheap
Eastern Europe
transformation
ex-communist
Black sea
Sofia
mountains
rich/poor
Sunny beach
Bulgaria
Number of 2nd order links Number of 3rd and higher order links
Number of 1st order links
Valence
Number of -
Number of 0
Number of +
Associations Bulgaria Sunny beach rich/poor mountains Sofia Black sea ex-communist transformation Eastern Europe cheap Golden Sands
Times included in the maps
Associations
2,30 1,55 1,44 2,22 2,63 2,58 2,74 2,67 2,53 2,11
15
2.17
Sunny beach (+)
Golden sands (+)
2 1.86
2 2.35
Black sea (+)
2.29
Cheap (+)
2.27
2.12
1.44
Mountains (+)
2.06 2.22
BULGARIA 1.81
Sofia (0)
Eastern Europe (0)
1.69
1.94
1.67 2
1.47
1.83 Rich/Poor (-)
Ex-communist (0) 2.17
Transformation (0)
1.60
Figure 1. Destination brand molecule of Bulgaria
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