Socio-Demographic Profile of Tourists in Cracow

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1 Copyright by Institute of Organization and Managment in Industry ORGMASZ Vol 3(1); p Year /v Socio-Demographic Profile of Tourists in Cracow Ewa Grabiñska * Introduction The purpose of this article is to present a unique profile of tourists who arrive at a destination by plane. This profile is set against a background of socio-demographic features of tourists who on the other hand arrive by other means of transport (car, train, coach, scheduled bus service). Based on the analysis performed it is possible to distinguish in terms of which specific features (gender, income, education, profession etc.) plane tourists differ from the remainder of tourists, or whether it is not possible to detect differences. The result of this study may be used to analyze the role and significance of airports development in age of tourist movement within the impact zone of a given airport. This especially pertains to segmentation of client-tourist model at the regional Balice Airport as well as it takes into account preliminary segmentation of client-tourist model who arrive at Balice Airport. For the purpose of this article a conventional definition of plane tourist has been devised. This type of tourist arrives in Krakow solely via plane. It should be pointed out that in scientific literature this particular term has not been used thus far, hence this definition is placed in footnote 1. Source of materials and analysis scope Studies presented in this article pertain to tourist movement in Krakow and have been carried out on the basis of survey research performed in These surveys were performed by the Malopolska Tourist Organization commissioned by the Krakow Municipal Department [2]. Survey study commenced in 2003 and has been continued for 4 years (until 2006). This study employed a homogenous method. In 2007 Krakow Municipal Department commissioned this study to another company, which subsequently employed a different study method and in consequence it was not possible to perform comparative analysis as well as examine development trends. In 2008 Krakow Municipal Department has returned to the previously employed methodology and commissioned the same institution (as in the beginning) to conduct research. Nevertheless, thus far there are no available source data to perform relevant analyses. Results of this study are posted in the form of reports for the period on Malopolska Tourist Organization website On the following website we can find information system, which has been elaborated by the Krakow Multimedia Company pollster.hybrid.pl. This system can be employed in the analysis of survey data, which had been collected during the study procedure. Furthermore, on this website krakow.pl files containing survey data are readily available. The data is accessible in the * Ewa Grabińska PhD, Department of Management in Tourism, Jagiellonian University. 1 World Tourist Organization for statistical purposes distinguishes two categories of travelers: visitors and other travelers. Visitor is each person who travels to a place outside his everyday environment for no longer than 12 months under the condition that his principal aim of journey is not undertaking economic activity, which is rewarded in the place he visits. Tourist is a person who travels outside his place of residence for a limited period of time, assuming that such a trip is voluntary and the traveler freely chooses his destination. Tourist realizes during his trip recreational, cognitive, health and entertainment goals, which are sponsored by the funds from other places [1]. 106

2 required POLLSTER format, and it is possible to export this data to Excel, from all existing editions. If we would like to access source data it is necessary to obtain an ID and password from Malopolska Tourist Organization. During the study procedure, which was performed in Krakow area, a total of 3242 surveys have been obtained. Assuming the procedure of statistical error selection this warranted appropriate precision order of study results (3% base error). The surveys were collected during the period from June to September, by professional employees, which had a 4-year survey experience. One of the principal tasks of this study was to estimate the value (volume) and structure of tourist arrivals to Krakow, by classifying them into 2 groups: domestic and foreign. The survey contained information, which enabled to characterize visits in terms of: their purpose, length of stay, travel companions, means of transport etc. In addition the study examined: average expense rate during stay, means of spending leisure time, level of satisfaction regarding tourist service (entertainment, cleanness), as well as service quality to price rate. Tourists were also asked to assess tourist attractions and respond whether they are willing to visit Krakow in the future. From considerable volume of information presented in the survey one question has been selected for in-depth analysis: What means of transport did a tourist use to come to Krakow? This question listed the following options (percentage of response given in parentheses): Regular scheduled bus service (3,3%), tourist coach (17,9%), train (13,5%), car (26,7%), plane (38,6%), other means of transport (bike, bicycle, hitchhike). Due to insignificant number of responses (19 out of 3242 which corresponds to 0,58%) it was decided that responses given in the other category would be excluded. Unfortunately the survey did not include a question pertaining to the type of airline (scheduled flights vs. low cost airlines). Therefore it was not possible to perform in-depth analysis of plane tourists. As indicated in 2006 most of the surveyed tourists selected plane (38%) as principal means of travel to Krakow, followed by car (27%), coach (18%) and train (13%). This study also displays profiles of individual tourist categories based on socio-demographic features. The certificate includes the following features: gender, education, age, professional status, income, polish nationality, place of residence Two additional questions have been included in the analysis, which are essentially important in terms of the general tourist opinion concerning Krakow: are you planning to visit Krakow again? will you recommend Krakow to your friends? 107

3 It should be mentioned that due to the methodological requirements outlined by the author (multidimensional matrix) tables which display results contain aggregated options for given responses. The reason for this was to avoid cases, where the number of responses was small, and under such circumstances share indices would not be entirely credible. Method for analyzing dependence In subsequent discussion the author employed tools for analysis of dependencies between qualitative features (two-step contingency matrix) and chi square test. The above-mentioned methods enable to determine whether a dependency exists between a given pair of features, what is the strength of such dependency, and how this dependency is expressed. Dependencies between selected features underwent interdependency analysis. In particular this refers to deciding on set of questions, which characterized tourists that had been surveyed and included a question posed to determine means of transport used to arrive in Krakow. Starting point for this analysis features contingency matrix, which contains absolute values: where: xij number of surveyed persons who selected i-th variant from the first question (side of the table) and j-th variant from the second question (head of table); xi. brim population of variants on the left side of table; x.j brim population of variants at the top of the table; n total number of surveyed persons. Based on the above-mentioned data we obtain two tables - percentage share in terms of columns sum (% columns) and percentage share in terms of rows sum (): Elements displayed in these tables indicate the percentage distribution of surveyed persons inside each population subgroup, which have been determined by subsequent variants of each feature, which is located either on the side or head of the table. These variants were determined separately for each tourist category and for each survey variant. Based on the elements of contingency matrix we calculate chi square test by employing the formula below: 108

4 xˆ where is the theoretical population of ij-th contingency matrix cell obtained by assuming i j entire independency of analyzed features. Theoretical values are expressed by the formula given below: Designated statistics value χ 2 (chi emp.) is compared with matrix value χ 2 α (chi tabl.) readout under the assumed significance level α=0,05 and (r-1)*(s-1) degrees of freedom. It should be noted that symbols r snd s signify number of columns and number of rows in the contingency matrix respectively. If χ 2 > χ 2 α, then we can assume with 0,95 probability that there exists a dependency between the analyzed features. In other cases no principles exist that would exclude a hypothesis in terms of lack of dependency. In addition we also designate the probability (p-ty) at which we are able to exclude a hypothesis concerning the lack of dependency between compared features. The parameters described above (i.e. chi tabl., p-ty and chi emp.) are given in the last column in the last part of each analytical tables, which have been attached at the end of this article. On the basis of these data we can determine whether a statistically significant dependency exists between analyzed features (chi emp.>chi tabl. and. p-ty<0,05). In this part of the result tables we can find the following indices (emp/exp-100) that have been determined on the bases of the formula given below: which present relative (expressed in percent) significance of differences between empirical values and expected ones (if we assume lack of dependence). Other words, these indices enable to determine how they are expressed and what is the possible dependency between the analyzed variables. Analysis results results of calculations performed are displayed in tables 1 through 7 (for subsequent certificate features) and in tables 8 and 9, for questions that wholly evaluate tourists stay in Krakow. It is clearly shown that in practically every single case high significant dependence between means of transport and socio-demographic features has been indicated. Empirical values for chi square test surpassed several times (5-10) matrices values. This simply means that tourists, who arrive to Krakow by plane, bus, train, and car, vary significantly among each other in terms of the distinguished features. This might constitute an argument for introducing to analysis a more comprehensive tourist delimitation scale in terms of what type of transport tourists choose when traveling. Only in case of the latter feature (will you recommend Krakow to your friends) a lack of dependence (0,05 significance level) has been indicated between tourist category and their responses to this question. However, if we decreased the level of possible response error (from 0,95 to 0,90) even then it would be possible to formulate a conclusion regarding statistical dependence between these particular features. 109

5 Interesting results are obtained from detailed exporation of individual tables. Gender (table 1) Largest differences are observed in case of tourists using buses and coach service (clear women advantage), meanwhile smaller differences are observed in case of tourist using cars and planes (advantage of men). Additionally total lack of differences in terms of gender is observed among train tourists. Education (table 2) Among tourist who use scheduled bus services (including coach service) a large advantage of persons with elementary and technical education is seen, as well as a clear absence of persons with higher education. In terms of the remaining transport means there is a slight advantage of people with higher education. Age (table 3) There is a clear advantage of elderly persons (above 60 years old) among plane tourists and at the same time lack of young people (less than 20 years old). An interesting profile of car tourists emerges from this analysis. There is an advantage of middle-aged persons (30-50 years old) and a large deficit of very young people (less than 20 years old) as well as elderly (above 60 years). It should be noted that in contrast to other demographic features profile of bus tourist differs from coach service tourist. Professional status (table 4) People who arrive in Krakow by plane are mainly retirees and pensioners, while very rarely students and unemployed. This last group (to smaller extent students) mainly chooses train, which is the cheapest means of transport. Students usually travel by coach services and scheduled bus services. Income (table 5) As expected plane tourists evaluate their material status as very good, and there is a lack of population in this group with lower incomes. Tourists, who are experiencing poor material status choose scheduled bus service, coach service and train, respectively. Car tourists are usually persons who possess an average material status. In all cases (except car) we can observe linear trends (fig. 1). We can observe an ideal convergence between tourist coach profile and train profile. Figure 1. Dependencies between actual and theoretical values, which correspond to means of transport and material status 110

6 Polish nationality (table 6) Tourists who arrive to Krakow by plane are primarily not of Polish descent. These are mainly foreign tourists. Tourists who come to Krakow by car mainly declare Polish nationality. An ideal balance is observed among coach tourists (but not in case of scheduled bus service). Place of residence (table 7) Plane constitutes means of transport in case of people living in large cities meanwhile there is an absence of people who live in villages and small cities. The latter arrive to Krakow mainly by bus and car. Is he/she planning to return to Krakow again (table 8) Tourists who arrive by plane differ substantially in their opinions (concerning their possibility of returning to Krakow as tourists) compared to other tourists categories. Among plane tourists there is a large group of people who declare that they do not want to return to Krakow and a deficit of people who would like to visit Krakow once again. An opposite situation takes place in case of tourists who arrive by car. They declare that they would rather like to return, and on the other hand there are no people among them who would not want to return to Krakow. In the remaining categories there are no discrepancies in the opinions. Will you recommend Krakow to your friends (table 9) In this particular case the dependence between means of transport and assessment of stay is clearly less visible than in the remainder of the questions. Nonetheless, it may be expressed (with a smaller impact factor) by a relatively large surplus of tourists who arrive to Krakow by plane, car and bus, but who will not recommend Krakow to their friends. Meanwhile the opinions concerning tourists who come to Krakow by train or coach are more favorable. Conclusions One of the most important conclusions that result from this study is the indication of high dependency between practically every socio-demographic feature analyzed in this study as well as means of transport utilized by tourist. It appears then that this particular characteristic is important in terms of diversification among tourist population. That is why it is essentially important in terms of marketing analyses to discover the distribution of preferences according to this feature (means of transport). Further analyses should determine the consistency level of various profiles of tourist categories. Preliminary observation suggests greater similarity between profiles of plane tourists and car tourists and consequently lack of similarity among train tourists. The remaining two categories are found in between (bus and coach tourists) and they are quite similar to each other. We should also enquire about the possibility of employing correspondence analysis in order to indicate which of the variants of individual socio-demographic features are correlated with one another and to what extent they are decisive in selecting means of transport. Another possibility for expanding the analysis is to include other features that have been listed in the survey (i.e. opinions about tourist service quality, means of spending leisure time during stay, length of stay, place of accommodation, size of expenses etc.) References 1. WTO and UNSTAT (1994). Recommendations on Tourism Statistics. New York: WTO, Madrid and United Nations. 111

7 2. Tourist movement research in Malopolska Province and Krakow in the years , Malopolska Tourist Organization, Retrieved from 3. Tourist Development Strategy for Krakow during the period , Krakow City Department, Retrieved from List of tables Table1. Gender Woman 63,8 62,8 51,8 48,5 48,7 52,1 Man 36,2 37,2 48,2 51,5 51,3 47,9 % columns Woman 4,0 21,6 13,4 24,9 36,1 100 Man 2,5 13,9 13,5 28,8 41,3 100 Emp/exp-100 9,5 Woman 22,5 20,6-0,5-6,9-6,5 0,000 Man -24,5-22,4 0,5 7,5 7,1 42,5 Table 2. Education Higher 29,5 39,3 53,9 52,2 55,4 50,6 Intermediate 51,4 42,6 39,4 41,0 38,2 40,3 Other 19,0 18,2 6,7 6,8 6,4 9,1 Higher 1,9 13,9 14,3 27,6 42,2 100 Intermediate 4,2 18,9 13,2 27,2 36,6 100 Other 6,8 35,8 9,9 20,1 27,3 100 Emp/exp-100 Higher -41,7-22,4 6,5 3,2 9,4 15,5 Intermediate 27,6 5,6-2,2 1,6-5,3 0,000 Other 109,5 99,8-26,5-24,7-29,3 117,5 112

8 Table 3. Age to 20 15,2 12,1 5,1 1,6 1,2 4, ,1 17,3 29,0 20,9 20,9 21, ,2 15,9 27,0 33,4 23,6 25, ,7 16,3 13,6 23,9 20,0 19, ,2 18,5 12,7 14,0 14,7 15, ,6 15,6 9,0 5,1 14,6 11,3 above70 2,9 4,3 3,7 1,0 5,0 3,6 to 20 11,7 51,1 16,1 10,2 10, ,5 14,2 17,9 25,5 36, ,0 11,4 14,5 35,7 36, ,0 15,3 9,6 33,6 40, ,3 22,2 11,4 25,1 38, ,5 24,7 10,7 12,1 50,0 100 Above 70 2,6 21,7 13,9 7,8 53,9 100 Emp/exp-100 to ,5 184,9 19,3-61,8-71, ,8-20,9 32,7-4,5-4, ,1-36,4 7,7 33,4-5, ,0-14,5-28,5 25,6 5, ,9 23,8-15,3-6,1-1,6 36, ,1 37,9-20,4-54,8 29,5 0,000 pow.70-19,9 21,2 3,3-70,7 39,7 346,8 113

9 Table 4. Professional status School student 15,2 13,0 3,7 2,0 1,1 4,3 College student 20,0 9,7 20,3 8,5 8,4 10,6 Home 3,8 5,2 5,1 8,4 6,5 6,5 Unemployed 3,8 1,6 3,2 1,5 0,8 1,6 Retiree 12,4 26,8 12,0 7,2 17,8 15,6 Professionally active 44,8 43,8 55,8 72,5 65,4 61,5 School student 11,6 54,3 11,6 12,3 10,1 100 College student 6,1 16,4 25,7 21,3 30,4 100 Home 1,9 14,4 10,5 34,4 38,8 100 Unemployed 8,0 18,0 28,0 26,0 20,0 100 Retiree 2,6 30,8 10,3 12,3 43,9 100 Professionally active 2,4 12,8 12,2 31,5 41,1 100 Emp/exp-100 School student 255,9 203,1-13,9-53,9-73,7 College student 88,5-8,7 91,1-20,2-21,2 Home -41,3-20,0-21,8 28,8 0,4 Unemployed 145,6 0,4 107,9-2,8-48,2 31,4 Retiree -20,7 71,8-23,2-53,9 13,8 0,000 Professionally active -27,2-28,8-9,3 18,0 6,5 403,5 114

10 Table 5. Income Very good 1,9 18,3 18,2 27,3 34,3 26,3 Good 42,9 52,1 53,9 50,5 54,4 52,5 Average 44,8 25,8 24,2 21,1 10,9 19,2 Poor 10,5 3,8 3,7 1,3 0,4 2,0 Very good 0,2 12,5 9,3 27,7 50,3 100 Good 2,7 17,8 13,8 25,7 40,0 100 Average 7,6 24,1 17,0 29,4 21,8 100 Poor 16,9 33,8 24,6 16,9 7,7 100 Emp/exp-100 Very good -92,8-30,4-30,9 3,5 30,3 Good -18,3-0,7 2,8-3,8 3,7 21,0 Average 133,4 34,4 26,2 10,1-43,4 0,0 Poor 419,5 88,7 82,8-36,7-80,1 254,9 Table 6. Polish nationality Yes 82,9 43,1 66,1 73,1 22,5 47,7 No/don t know 17,1 56,9 33,9 26,9 77,5 52,4 Yes 5,7 16,2 18,7 41,0 18,2 100 No/don t know 1,1 19,5 8,7 13,7 57,

11 Sum Emp/exp-100 Bus Coach Train Car Plane 9,5 Yes 20,1-0,2 12,0 62,1-87,3 0,000 No/don t know -16,8 0,2-10,1-52,1 71,2 654,5 Table 7. Place of residence Large city 38,1 52,9 65,4 51,5 75,8 62,6 Medium-sized city 49,5 42,2 30,9 41,9 21,4 32,8 Village 12,4 4,8 3,7 6,6 2,8 4,6 Large city 2,0 15,2 14,1 22,0 46,8 100 Medium-sized city 4,9 23,1 12,7 34,2 25,2 100 Village 8,7 18,8 10,7 38,3 23,5 100 Emp/exp-100 Large city -39,1-15,4 4,6-17,7 21,1 21,0 Medium-sized city 51,0 28,7-5,9 27,7-34,8 0,000 Village 167,8 4,8-20,3 43,0-39,1 197,8 Table 8. Is he/she planning on returning to Krakow Yes 65,7 41,7 59,2 58,4 37,3 47,7 Probably yes 32,4 53,6 34,1 39,7 51,4 46,0 Probably not/no 1,9 4,7 6,7 2,0 11,3 6,8 Yes 4,5 15,7 16,7 32,7 30,2 100 Probably yes 2,3 20,9 10,0 23,0 43,1 100 Probably not/no 0,9 12,3 13,2 7,7 64,

12 Emp/exp-100 Yes 2,7-0,6 2,9 4,6-6,2 21,0 Probably yes -1,3 1,2-3,5-1,1 0,9 0,000 Probably not/no -2,7-1,2 0,0-21,2 24,5 186,9 Table 9. Will he recommend Krakow to his friends Yes 90,5 93,8 94,0 90,6 90,8 91,7 No/don t know 9,5 6,2 6,0 9,4 9,2 8,3 Yes 3,2 18,3 13,8 26,4 38,2 100 No/don t know 3,7 13,4 9,7 30,2 42,9 100 Emp/exp-100 9,5 Yes -1,3 2,3 2,5-1,2-1,0 0,054 No/don t know 14,5-25,1-28,0 13,0 11,2 9,3 117