The factors of service quality in ski tourism on the example of the Republic of Serbia

The subject of this article deals with the research of service quality in ski tourism and the main factors that affect tourist satisfaction in ski destinations. Ski tourism has evolved into an important segment of the winter tourism industry attracting around 350 million skier visits annually (Steiger et al. 2019). Its development was encouraged by the popularisation of skiing as a sport, as well as the growth of the number of people who ski professionally or recreationally. One of the reasons for the popularization of this type of tourism is that skiing, which was once an elite sport, has now become more accessible to the wider population, which has had a positive effect on the growth of the ski tourism market.

Bieger et al. (2002) define the tourist destination of winter sports as the geographical, economic, and social unit, consisting of organisations, companies, activities and infrastructure, having as a goal to enable the satisfaction of specific needs of winter sports tourists. The most popular ski destination in the world are the Apls, with the so called “Big Four” ski countries that are situated in this region: Switzerland, Austria, Italy, and France. According to Statista (2023) around 37 percent of all ski resorts worldwide are in Alpine countries.

Literature review

In exploring the concept of service quality in ski tourism, this paper was focused on the segment of recreational ski tourists, which has certain specificities. Theodorakis et al. (2009) in their study conducted a segmentation of recreational ski tourists according to motives, dividing them into four categories: beginners, lovers, naturalists, and tourists with multiple interests. Among the four categories of ski tourists, the authors have identified significant differences both in terms of loyalty and participation in the activities. The category of ski lovers had statistically higher results in engagement in activities compared to the remaining three groups of tourists.

The survey presented in this paper has included an analysis of different determinants (variables) of service quality in ski tourism which were successfully tested in practice and presented in cited literature. Having in mind the comprehensiveness of studies, they were used as a role model in designing the questionnaire for this research. However, the survey was additionally enriched with two service quality determinants referring to servicscape and nature.

Methods and sample

The theoretical research presented in this paper was based on the application of a systematic literature review that has included relevant studies dealing with the concept of service quality in winter sport tourism. Empirical research was conducted to address the research question in finding the factors that influence the service quality in ski tourism. The survey has included a sample of 208 (N=208) recreational skiers who visited the Republic of Serbia during the winter seasons in 2023 and 2024. The questionary was designed in accordance with the models presented in the literature review and included 18 statements (variables) referring to specific determinants of service quality in ski tourism. The variables were examined in the form of closed questions using a Likert scale with the following values: 1 = disagree; 2 = slightly disagree; 3 = neither agree nor disagree; 4 = slightly agree; 5= agree. In the processing and interpretation of the data, methods of factor analysis and compared means were applied.

Table 1 represents the sample structure by gender. Respondents were equally represented in the sample (N=208), with a slight majority of the male population (51,9%).

Table 1 : The sample structure by gender

Gender

Frequency		Percent	Valid Percent	Cumulative Percent
Male	108	51.9	51.9	51.9
Valid Female	100	48.1	48.1	100.0
Total	208	100.0	100.0

Source: Authors’ calculation in SPSS

The age distribution of the sample (table 2) is dominated by the respondents belonged to the 36-45 age category (62.5%) followed by the 47 -56 (15,4%), 57-65 (12%) and 2035 (10.1%) age groups.

Table 2: The age categories of respondents

Age

	Frequency	Percent	Valid Percent	Cumulative Percent
20-35	21	10.1	10.1	10.1
36-46	130	62.5	62.5	72.6
Valid 47-56	32	15.4	15.4	88.0
57-65	25	12.0	12.0	100.0
Total	208	100.0	100.0

Source: Authors’ calculation in SPSS

In relation to the country of residence, the respondents were from the following countries: Serbia (54.3%), Bosnia and Herzegovina (19.7%), Russia (9.1%), Montenegro (9.6%), North Macedonia (3.8%), Croatia (3,4%). Detailed data on this variable are shown in table 3.

Table 3: Respondents’ countries of residence

Country of residence

	Frequency	Percent	Valid Percent	Cumulative Percent
Serbia	113	54.3	54.3	54.3
Bosnia and Herzegovina	41	19.7	19.7	74.0
N.Macedonia	8	3.8	3.8	77.9
Valid Montenegro	20	9.6	9.6	87.5
Russia	19	9.1	9.1	96.6
Croatia	7	3.4	3.4	100.0
Total	208	100.0	100.0

Source: Authors’ calculation in SPSS

Research results

The respondents have ranked the importance of 18 variables that affect their perception of service quality in ski tourism. The variables have included different aspects such as facilities of ski resorts, snow conditions and ski tracks, additional services including shops and restaurants, accommodation, social life, landscape, and nature. To reduce the number of variables for further exploration, the method of factor analysis was applied at the beginning of this research.

Having in mind the data demonstrated in table 4 showing that KMO=0.748 is greater than 0.6, and Bartlett's Test of Sphericity is statistically significant (p=0.000), the factor analysis was considered justified in this study.

Table 4: KMO and Bartlett's Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.       .749

Bartlett's Test of Sphericity	Approx. Chi-Square	3108.259
	Df	153
	Sig.	.000

Source: Authors’ calculation in SPSS

Table 5: Total Variance Explained

Total Variance Explained

Rotation

Com- po- nent Total	Initial Eigenvalues % of Cumulative % Variance		Extraction Sums of Squared Loadings Total % of     Cumulative % Variance			Sums of Squared Loadingsa Total
1     5.609	31.163	31.163	5.609	31.163	31.163	4.112
2     3.781	21.006	52.170	3.781	21.006	52.170	4.316
3     1.896	10.531	62.701	1.896	10.531	62.701	2.753
4     1.563	8.682	71.383	1.563	8.682	71.383	3.281
5     1.088	6.045	77.428	1.088	6.045	77.428	1.327
6      .925	5.139	82.567
7      .687	3.816	86.383
8      .588	3.267	89.650
9      .385	2.140	91.790
10    .367	2.040	93.829
11    .271	1.506	95.336
12    .203	1.129	96.465
13    .167	.929	97.394
14    .132	.736	98.129
15    .121	.674	98.803
16    .096	.533	99.336
17    .067	.373	99.709
18    .052	.291	100.000

Extraction Method: Principal Component Analysis.

a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.

Source: Authors’ calculation in SPSS

The table of Total Variance Explained (table 4) indicated five factors that have eigenvalues higher than 1. Together they account more than 77% of the variability in the original variables. In addition, the Eigenvalues and component numbers are demonstrated on a Scree Plot in figure 1.

Figure 1 : Scree Plot

Source: Authors’ calculation in SPSS

Table 6: Component Matrix

Component Matrix ^a

	1	2	Component 3	4	5
Diversity of ski tracks	.832		.366
Accommodation prices	.820	.360
Lift maintenance and functioning	.812			-.440
Restaurants prices	-.772			-.429
Quality of the slopes	.756
Facilities quality	.668	-.564
Meeting different people	-.651	-.357
Supermarkets and shops	.649		.500	.398
Nightlife	.615	.556	-.325
Competence of the employees and hospitality	.453		.404
Restaurant quality		.862	-.329
State of equipment	-.342	-.802
Snow conditions		.755	.470
Parking		.548	.421	-.306

Accommodation quality	.419		-.708	.328
Health and safety services	-.415	.561		.593
Nature					.779
Landscape		.331		.402	.406

Extraction Method: Principal Component Analysis. a. 5 components extracted.

Source: Authors’ calculation in SPSS

According to the findings presented in table Component Matrix the variables were grouped within 5 factors. The first factor was highly associated with the following determinants: diversity of ski tracks, the quality of slopes, lift maintaining and functioning, the prices of accommodation and restaurants, facility quality, meeting different people and nightlife. Within the second factor the variables such as restaurant quality, state of equipment, parking and snow conditions were recognised as the most dominant. The third factor was associated with supermarket and shops and accommodation quality, while the fourth factor has the highest association with health and safety services. The fifth factor was related to nature and landscape.

Further research was conducted to compare the respondents’ answers regarding the variables that were highly associated with the first factor. The data presented in table 7 demonstrate the compared means in variables between the ski tourists from different countries of residence. When interpreting the data it should be noted that higher representation of domestic tourists (54.3%) was represented in the sample, The highest mean values in answers were registered for variables “diversity of ski tracks” (Total M=4.89), “facilities quality” (Total M=3.93) and “quality of the slopes” (Total M=3.53).

For variable “lift maintenance and functioning” the highest mean was recorded among the respondents from Serbia (M=3.08, SD=0.73) in comparison with the lowest mean value among the respondents from North Macedonia (M=2.25, SD=0.46). Diversity of ski tracks was recognised as the most important determinant, with the highest mean values between the tourists from Bosnia and Herzegovina (M=4.97, SD=15), Montenegro (M=4.95, SD=0.22), Serbia (M=4.94, SD=0.22) and Russia (M=4.84, SD=0.37). Furthermore, respondents from Montenegro had the highest means for variables “quality of the slopes” (M=3.75, SD= 0.55) and “nightlife” (M=3.7, SD=0.73). Ski tourists from North Macedonia had highest mean values for variables “restaurants prices” (M=4.5, SD=0.92) and “meeting different people” (M=4, SD=0), while respondents from Serbia highly appreciated “facilities quality” (M=4.12, SD=0.66) as a determinant of service quality in ski tourism. The findings presented in table 7 are statistically significant according to ANOVA table (p=0.00) for selected variables.