Electronic copy available at: http://ssrn.com/abstract=1603494

1

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: stanislav.ivanov@vumk.eu

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

Electronic copy available at: http://ssrn.com/abstract=1603494

2

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). Non-

quantitative 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

3

(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. Twenty-

two 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

4

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

5

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:

● 2

1

MR i - in more than one-half of the individual molecules collected, the i-th

association is mentioned as a first-order association

● CCi - the i-th association has a higher than the average total number of connections

with other associations

● CC i - 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:

● 2

2

MR i - in more than one-half of the individual molecules the i-th association is

mentioned as a second-order association

● CCi - the i-th association has a higher than the average total number of connections

6

with other associations

● CC i - 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 – 5.1;1ijL

● medium –- 5.2;5.1ijL

● strong – 3;5.2ijL

→Step 4.5.: Determination of the valence of an association in the consensus molecule:

● positive – 1;5.0iV

● neutral – 5.0;5.0iV

● negative – 5.0;1iV

→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

7

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.

8

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.

9

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.

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12

Table 1. Descriptive statistics in individual association lists

Statistic Value

Total number of associations 64

Times all associations mentioned 140

Average times one association mentioned 2.19

Total number of association lists 22

Average length of one association list 6.36

Table 2. Aggregated association lists

Association Times mentioned Percent mentioned

Bulgarian respondents (n=22)

Black sea 12 54,55%

Sunny beach 9 40,91%

Sofia 8 36,36%

ex-communist 5 22,73%

cheap 5 22,73%

rich/poor 4 18,18%

mountains 4 18,18%

transformation 4 18,18%

Eastern Europe 4 18,18%

Golden Sands 4 18,18%

parties 3 13,64%

football (in 1990s) 3 13,64%

good food 3 13,64%

nature 3 13,64%

friendly people 3 13,64%

Orthodox 3 13,64%

USSR 3 13,64%

destroyed streets 2 9,09%

crimes 2 9,09%

8 million inhabitants 2 9,09%

chaotic traffic 2 9,09%

thermal springs 2 9,09%

sun and beach 2 9,09%

Varna 2 9,09%

monasteries 2 9,09%

big hotels 2 9,09%

good wine 2 9,09%

hospitality 2 9,09%

European union 2 9,09%

beautiful landscape 2 9,09%

differences 1 4,55%

waste 1 4,55%

ruins 1 4,55%

agriculture 1 4,55%

warmer weather than Germany 1 4,55%

folklore 1 4,55%

13

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

14

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

molecules

N

Times i-th association mentioned in individual molecules Ni

Times the connection between i-th and j-th associations

mentioned

Nij

Strength of connection between the i-th and j-th associations in

a particular molecule

Lij

Average strength of connection between the i-th and j-th

associations in all molecules ij

ijij

N

LL

Valence of i-th association Vi

Average valence of i-th association in all molecules

i

i

i

iN

V

V

Number of connections of i-th association with other

associations j

iji NC

Average number of connections of i-th association with other

associations per one molecule i

ii

N

CC

Average total number of connections of one association in all

molecules

N

C

C i

i

Total average number of connections of one association per

one molecule

i

i

i

i

N

C

C

Number of first-order connections of an association – times the

association mentioned in all molecules with a direct connection

with Bulgaria

R1i

Number of second-order connections of an association – times

the association has connections with a first-rank association in

all molecules

R2i

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

R3i

15

Table 4. Aggregated statistics from the individual brand molecules of Bulgaria

Associations Tim

es i

ncl

uded

in t

he

map

s

Num

ber

of

+

Num

ber

of

0

Num

ber

of

-

Val

ence

Num

ber

of

1st

ord

er l

inks

Num

ber

of

2nd o

rder

lin

ks

Num

ber

of

3rd

and h

igher

ord

er l

inks

Associations

Tota

l num

ber

of

connec

tions

Aver

age

num

ber

of

connec

tions

per

on

e

mole

cule

Bulg

aria

Sunny b

each

rich

/poor

mounta

ins

Sofi

a

Bla

ck s

ea

ex-c

om

munis

t

tran

sform

atio

n

Eas

tern

Euro

pe

chea

p

Gold

en S

ands

Bulgaria - - - - - - - - - 17 11 16 18 17 16 16 16 16 15

Sunny beach 20 16 2 2 1 17 3 0 17 - 0 1 1 14 0 0 0 7 6 46 2,30

rich/poor 20 0 1 19 -1 17 3 0 11 0 - 0 6 0 4 5 3 2 0 31 1,55

mountains 18 13 5 0 1 16 2 0 16 1 0 - 3 1 0 1 1 2 1 26 1,44

Sofia 18 6 11 1 0 18 0 0 18 1 6 3 - 2 4 1 2 2 1 40 2,22

Black sea 19 16 3 0 1 17 2 0 17 14 0 1 2 - 0 1 1 3 11 50 2,63

ex-communist 19 4 6 9 0 16 3 0 16 0 4 0 4 0 - 12 9 4 0 49 2,58

transformation 19 12 5 2 1 16 3 0 16 0 5 1 1 1 12 - 13 3 0 52 2,74

Eastern Europe 18 3 11 5 0 16 2 0 16 0 3 1 2 1 9 13 - 3 0 48 2,67

cheap 19 15 3 1 1 16 3 0 16 7 2 2 2 3 4 3 3 - 6 48 2,53

Golden Sands 19 15 4 0 1 15 4 0 15 6 0 1 1 11 0 0 0 6 - 40 2,11

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

16

BULGARIA

Golden sands (+)

2.29

2.12

Figure 1. Destination brand molecule of Bulgaria

Cheap (+)

Eastern Europe (0)

Transformation (0)

Ex-communist (0)

Sofia (0)

Mountains (+)

Rich/Poor (-)

Sunny beach (+)

Black sea (+) 2.27

2.17

2

2.35

1.86

2

2.06

1.44

2.22

1.47 1.83 1.94

1.81

1.69

1.67

2

2.17 1.60