Finding the key to success: A visitors' perspective at a National Arts Festival (original) (raw)

Abstract

Purpose and/or objectives: The purpose of this article was to segment festival visitors at the Klein Karoo National Arts Festival (KKNK) based on their travel motives and their ratings of the Key Success Factors (KSFs) in terms of their festival experience. Problem investigated: Previous research has indicated that the success and sustainability of an arts festival is dependent on the number of tickets sold for shows and productions during the festival. Therefore, success depends on attracting visitors who attend and buy tickets for different types of shows and productions. To achieve this festival organisers need to understand the aspects that visitors regard as satisfying their needs and which create a unique festival experience. Methodology: A survey was conducted using a questionnaire at the festival. A total of 450 questionnaires were administered and 443 completed questionnaires were included in the analysis. Factor analysis was used to identify visitors' motivation to tra...

Figures (9)

Figure 1: Relationship between planning and feedback

Figure 1: Relationship between planning and feedback

Table1: Factor analysis of the key success factors

Table1: Factor analysis of the key success factors

The factor scores were calculated as the average of all items contributing to a specific factor in order to interpret them on the original 5-point Likert scale of measurement (1 = Totally disagree; 2 = Do not agree; 3 = Neutral; 4 = Agree; 5 = Totally agree). As shown in Table 2, the following KSFs were identified: Safety and Personnel, Marketing and Accessibility, Venues, Accommodation and Ablution, Activities and Community, Parking and Restaurants and Quality Shows and Stalls. Shows and Stalls, Venues and Safety and Personnel were rated the most important according to visitors at the KKNK. Shows and Stalls and Parking and Restaurants were furthermore identified as unique success factors for arts festivals. Marketing and Accessibility and Accommodation and Ablution were rated the least important. The afore-mentioned contributes to the overall experience of visitors at the festival.

The factor scores were calculated as the average of all items contributing to a specific factor in order to interpret them on the original 5-point Likert scale of measurement (1 = Totally disagree; 2 = Do not agree; 3 = Neutral; 4 = Agree; 5 = Totally agree). As shown in Table 2, the following KSFs were identified: Safety and Personnel, Marketing and Accessibility, Venues, Accommodation and Ablution, Activities and Community, Parking and Restaurants and Quality Shows and Stalls. Shows and Stalls, Venues and Safety and Personnel were rated the most important according to visitors at the KKNK. Shows and Stalls and Parking and Restaurants were furthermore identified as unique success factors for arts festivals. Marketing and Accessibility and Accommodation and Ablution were rated the least important. The afore-mentioned contributes to the overall experience of visitors at the festival.

Table 2: Factor analysis results of KKNK visitors’ travel motivations

Table 2: Factor analysis results of KKNK visitors’ travel motivations

Identification of segmented clusters EE’ ep EEE DE IEEE IE! IIE ANOVAs of the three factors indicate statistical significant differences (p<0.001). ANOVAs indicated that all three factors contributed to differentiating between the three motivational clusters (p<0.05). In addition, Tukey’s post hoc multiple comparisons were employed to explore these differences between clusters with regard to each factor. Table 3 indicates differences in means between the three clusters and reveals the importance of each of the factors for festival travel for the members of each cluster. An exploratory cluster analysis based on all cases in the data was performed on the motivational factors. A hierarchical cluster analysis, using Ward’s method of Euclidean distances, was used to determine the clusters’ structures based on the motivation factors. A two-, three- and four-cluster solution was examined, and the three cluster solution was selected as the most discriminatory (Figure 2). The results of the multivariate analyses were used to identify the three clusters and to indicate that significant differences existed between them (p<0.05).

Identification of segmented clusters EE’ ep EEE DE IEEE IE! IIE ANOVAs of the three factors indicate statistical significant differences (p<0.001). ANOVAs indicated that all three factors contributed to differentiating between the three motivational clusters (p<0.05). In addition, Tukey’s post hoc multiple comparisons were employed to explore these differences between clusters with regard to each factor. Table 3 indicates differences in means between the three clusters and reveals the importance of each of the factors for festival travel for the members of each cluster. An exploratory cluster analysis based on all cases in the data was performed on the motivational factors. A hierarchical cluster analysis, using Ward’s method of Euclidean distances, was used to determine the clusters’ structures based on the motivation factors. A two-, three- and four-cluster solution was examined, and the three cluster solution was selected as the most discriminatory (Figure 2). The results of the multivariate analyses were used to identify the three clusters and to indicate that significant differences existed between them (p<0.05).

Note: respondents were asked to indicate how important they considered each item on the scale (1=not at all important, 2=less important, 3=important, 4=very important and 5=extremely important). Tukey’s multiple comparisons indicate that statistically significant differences exist among the clusters with different superscripts. For example, in terms of Festival attractiveness, differences were found between Cluster 1 (superscript a) and all the other clusters. Cluster 2 (superscript b) differed from all the other clusters and Cluster 3 (superscript c) differed from all the other clusters as well.

Note: respondents were asked to indicate how important they considered each item on the scale (1=not at all important, 2=less important, 3=important, 4=very important and 5=extremely important). Tukey’s multiple comparisons indicate that statistically significant differences exist among the clusters with different superscripts. For example, in terms of Festival attractiveness, differences were found between Cluster 1 (superscript a) and all the other clusters. Cluster 2 (superscript b) differed from all the other clusters and Cluster 3 (superscript c) differed from all the other clusters as well.

— otaistically significant aimerence: P<U.U0 Tukey’s multiple comparisons indicate that statistically significant differences exist among the clusters with different superscripts. For example, in terms of Festival attractiveness, differences were found between Cluster 1 (superscript a) and all the other clusters. Cluster 2 (superscript b) differed from all the other clusters and Cluster 3 (superscript bc) differed from all the other clusters as well.

— otaistically significant aimerence: P<U.U0 Tukey’s multiple comparisons indicate that statistically significant differences exist among the clusters with different superscripts. For example, in terms of Festival attractiveness, differences were found between Cluster 1 (superscript a) and all the other clusters. Cluster 2 (superscript b) differed from all the other clusters and Cluster 3 (superscript bc) differed from all the other clusters as well.

Acta Commercii 2012

Acta Commercii 2012

* Statistically significant difference: p<0.05

* Statistically significant difference: p<0.05

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