Beaches attract millions of visitors every year and this has an impact not only on the economy, the environment and the local community but also on current and future beachgoers.
The aim of the article is to use expenditure-based segmentation to gain a greater understanding of beach visitor spending in order to identify different markets and the aspects they deem important.
Although South Africa has a coastline of approximately 3900 km, little is known about the beachgoer to these beaches. This article contributes to the quantification of the economic benefits stemming from beach tourism and in understanding the factors that drive beach visitation.
During 2017/18, beachgoers to eight beaches in South Africa were surveyed and 1138 questionnaires were gathered. Using cluster analysis, four segments of beachgoers were identified. The differences between the various segments were explored by using analysis of variance and Pearson’s chi-square.
The high-spending markets can be distinguished by language, level of education and age, while sunbathing and relaxation are key beach activities. Six key motives for visiting beaches were identified. High-spending markets tend to visit Blue Flag beaches that offer good bathing conditions.
This research identified four segments of visitors to South African beaches, ranging from low-spending locals to high-spending mixed market beachgoers. There are distinct differences between the segments, but important to all segments are the environment qualities of the beach in terms of both cleanliness and beach safety.
The research, therefore, concludes that two the main threats to beach tourism and destinations are (1) pollution of the oceans and (2) global warming.
Tourism constitutes the world’s largest commercial service sector industry and beaches are considered the major factor in this tourism market (Houston
Lucrezi and Saayman (
Conceptual marine tourism framework.
The conceptual MTF highlights the fact that many stakeholders spend money in some way or another; this article focusses on the spending patterns of beach tourists/visitors. Their spending is influenced by socio-demographic and behavioural variables as well as external factors, for example how well the beach is known or promoted, the distance to the beach, and what the beach has to offer in terms of activities. These external factors can be viewed as ‘pull factors’ that entice beachgoers to choose this specific beach, and therefore include unique beach attributes. Interestingly, most studies in this field of research show that behavioural variables (which include group size, nights spent, frequency of travel and activity participation) have a greater impact on spending than socio-demographic variables (Saayman & Saayman
Beaches are in many instances seen as icons, a view that is also true for island destinations; this contributes to their value and is the reason why so many people visit beaches each year (James
Therefore, the purpose of this article is to apply expenditure-based segmentation to beach tourists visiting South African beaches. The article contributes to the quantification of the economic benefits stemming from beach tourism and in understanding the factors that drive beach visitation.
The reason for applying expenditure-based segmentation is well documented in the literature. In this regard, Lima, Eusébio and Kastenholz (
Therefore, the purpose of expenditure segmentation is motivated by the following reasons (Botha et al.
Although studies on expenditure patterns among visitors and tourists have been valued by planners, marketers and business managers, studies on expenditures remain limited (Jang et al.
Market segmentation is seen as one of the most critical concepts for businesses and is based on the premise that heterogeneity in demand functions exist in such a way that market demand can be disaggregated into segments with distinct demand functions (Dickson & Ginter
Based on the literature, the first attempt at expenditure-based segmentation was done by Pizam and Reichel (
Although most of the studies conducted on expenditure-based segmentation focus on a combination of low to high spenders, it seems that when it comes to specialised markets, different combinations exist and therefore a more detailed distinction between expenditure groups is required. For example, on the topic of mountain tourists, Lima et al. (
According to the literature review, high spenders can be distinguished from the medium and low segments by the following socio-demographic characteristics:
Firstly, they are better educated, having obtained higher levels of education, according to Hong et al. (
Secondly, they are employed in higher-income occupations (Cannon & Ford
Thirdly, they are older (Cal, Hong & Morrison
Fourthly, they often stem from a specific province, country or place of residence (Kruger et al.
Fifthly, from a gender perspective, it is mostly found that females spend more than males (Craggs & Schofield
Finally, language also plays a role in distinguishing high spenders from the rest in a multilingual destination or country (typically South Africa) (Saayman & Krugell
The behavioural variables that distinguish high spenders from the rest include the following:
Firstly, they travel specifically or primarily to visit the specific destination or an event (Botha et al.
Secondly, some researchers find that they travel in smaller groups (Kruger
Thirdly, they stay longer at the destination (Downward & Lumsdon
Fourthly, they travel longer distances to reach the destination (Cannon & Ford
Fifthly, the frequency of visits or participation indicates that high or big spenders are those who visit or participate more frequently (Botha et al.
In the sixth instance, the mode of transport also influences spending with Shani et al. (
Besides the above socio-demographic and behavioural variables, Saayman and Saayman (
Most of the studies on expenditure-based segmentation applied one dependent variable. However, Sung, Morrison and O’Leary (
The research followed a quantitative, descriptive and non-experimental design, using a structured questionnaire survey that targets beachgoers. The questionnaire was based on the works of Kruger (
The questionnaire included both close-ended and open-ended questions on socio-demographic, behavioural and local beach information. The socio-demographic questions assessed the gender, age, marital status, occupation, level of education and origin of the respondent. They also included eight spending categories, namely accommodation, shopping, restaurants, transport, activities, entertainment, curios/souvenirs and other in order to get a more detailed picture of spending behaviour. The respondent was also asked to indicate the number of people that this spending accounts for, since it is important to assess spending per person.
Besides the socio-demographic questions, questions on behaviour were also assessed, including travel group size and nights spent close to the beach. The respondents were asked to indicate why they visit the beach, with the following activity participation categories provided: sunbathing, swimming, surfing, walking and other. In addition, 22 motivational statements for choosing the specific beach were tested on a four-point Likert scale, ranging from 1 = disagree to 4 = strongly agree. The motivational statements were adapted from the study of Lucrezi and Saayman (
The questionnaires were randomly distributed on the beaches by a group of five trained field workers at the different destinations. Eight locations in South Africa were selected for this study (see
Map indication location of beaches.
To identify high-spending and low-spending beachgoers, a two-step clustering approach was used. In general, there are two approaches to cluster analysis, namely hierarchical and non-hierarchical methods. Hierarchical methods can accommodate scale (or numeric) data and use distance measures to form clusters of similar cases. Non-hierarchical methods require the prior selection of the optimum number of clusters and can only form clusters by using the same type of data (i.e. nominal, ordinal or numeric) (Norušis
Since the variable of interest is expenditure (a numeric variable) per person, which can be prone to outliers, additional variables were included to form sensible clusters, namely whether or not a respondent is a South African citizen and whether or not he/she is a local resident (i.e. international vs. local resident vs. visitors from the rest of South Africa). Since total expenditure per person was determined as the sum of spending on different aspects of the trip (accommodation, transport, food etc.) and then divided by the number of persons for which the respondent is financially responsible during the trip, only questionnaires that had complete information on these could be used. This rendered an adjusted sample of approximately 600.
Secondly, the motivational statements were reduced by using principal component analysis. The Kaiser-Meyer-Olkin measure indicated that the sample was sufficient to allow principal component analysis (KMO of 0.884), while Bartlett’s test of sphericity was also significant (
To explore the differences between the clusters, one-way analysis of variance (ANOVA) was used for the scale variables. Both Bonferroni’s and Tamhane’s T2 post hoc tests were performed to determine pairwise differences between clusters, with the Bonferroni test controlling for Type 1 errors and Tahmane’s test being more powerful when groups are unequal in size. To explore differences between clusters when the variables are nominal or ordinal, Person’s chi-square was used, as well as Cramer’s V as an approximate effect size (Field
This research project received ethical clearance and the ethics project number EMS2016/11/04-02/19 was assigned. This acceptance deems the proposed research as being of minimal risk, granted that all requirements of anonymity, confidentiality and informed consent are met, which was adhered to during the fieldwork.
The results are presented in three sections: first, the results of the two-step cluster analysis, followed by the results of the principal component analysis, and then an exploration of the differences between clusters, as explained above. A brief description of the sample is firstly provided (the data summary tables can be found in
The average age of the respondents in the sample was 34 years old. Most of the respondents (59%) were female and most respondents (49%) were English-speaking. A total of 34% of the respondents had at least a diploma or degree, with 20.5% also in possession of post-graduate qualifications. The majority of respondents (53%) were single and most respondents were in paid occupations (65%).
In terms of the origin of respondents, 35% were foreigners, whereas 20% were from the Western Cape province – one of the provinces in which a number of beaches are located. A large percentage (15.4%) of respondents came from Gauteng, which is a landlocked province; these beachgoers travel to the beach mainly during school holidays. Less than one-quarter of the sample are locals living in the beach vicinity.
Respondents travel in groups of between three and four (an average of 3.6 members per group), spend an average of 14 nights or two weeks at the beach (note that nights spent at the beach for locals were recorded as not applicable), and spend just more than ZAR7000 per person while visiting the beach. As can be expected, people visit the beach more regularly during summer months than winter months.
The two-step cluster analysis with numeric input of spending per person and the categorical inputs of South African versus non-South African residents, as well as locals versus non-local residents of the beach area, delivered four distinct clusters. The clusters and their membership are summarised in
Results of the cluster analysis.
Cluster | Per cent | Mean spending | Standard deviation | Non-RSA | Local residents | |
---|---|---|---|---|---|---|
1 – local low spenders | 128 | 21.6 | 3571.74 | 5453.62 | 0 | 128 |
2 – mixed high spenders | 31 | 5.2 | 43376.02 | 34134.57 | 26 | 13 |
3 – foreigners | 184 | 31.1 | 7114.29 | 7598.06 | 184 | 0 |
4 – South Africans | 249 | 42.1 | 4127.95 | 4640.64 | 0 | 0 |
Combined | 592 | 100 | 6991.10 | 12956.92 | 210 | 141 |
The second largest cluster in terms of membership is cluster 3 (184 members; i.e. 31.1% of the sample) and this cluster consists exclusively of foreign tourists visiting South African beaches. The cluster has a mean spending per person of just more than ZAR7000. Cluster 1 is the third largest with 21.6% of the members (128 respondents) belonging to this cluster. It is the cluster with the lowest spending per person (ZAR3571.74) and consists exclusively of local residents of the various beach areas.
Finally, the smallest cluster is cluster 2, with only 5.2% of the total membership (31 respondents); this cluster boasts the highest-spending per person – more than ZAR40 000. The cluster is mixed in terms of the origin of its members and is the only cluster that contains members of all three origins – international, South African and local residents.
Model summary.
The motivational statements were subjected to a principal component analysis and six factors with eigenvalues greater than unity were identified, which explains 58.425% of the variance. The different components are identified in
Results of the principal component analysis.
Variables | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
Swimming experience | Beach quality | Beach connectivity | Beach activities | Blue Flag & popularity | Accessibility | |
It has good swimming conditions | 0.643 | - | - | - | - | - |
The different types of people who come here | 0.426 | - | - | - | - | - |
It provides safe swimming conditions | 0.746 | - | - | - | - | - |
The sea produces good waves | 0.579 | - | - | - | - | - |
I feel safe here | - | 0.652 | - | - | - | - |
The water quality is overall good | - | 0.709 | - | - | - | - |
The beach is well managed | - | 0.540 | - | - | - | - |
The beach is clean | - | 0.825 | - | - | - | - |
I feel a special connection with this beach | - | - | 0.552 | - | - | - |
To break away from routine | - | - | 0.905 | - | - | - |
I had good previous experiences | - | - | 0.696 | - | - | - |
It is not too crowded | - | - | 0.543 | - | - | - |
There is more than enough parking | - | - | - | 0.599 | - | - |
There is a high number of water activities | - | - | - | 0.566 | - | - |
Various events are hosted here | - | - | - | 0.598 | - | - |
I wanted to explore new places | - | - | - | 0.800 | - | - |
The beach has Blue Flag status | - | - | - | - | 0.817 | - |
The beach is popular | - | - | - | - | 0.570 | - |
The beach has a beautiful natural setting | - | - | - | - | 0.373 | - |
Many people come to this beach | - | - | - | - | 0.467 | - |
The beach is close | - | - | - | - | - | 0.907 |
It is easily accessible | - | - | - | - | - | 0.395 |
Mean | 2.746 | 3.076 | 2.957 | 2.634 | 2.935 | 2.995 |
Cronbach’s alpha | 0.762 | 0.801 | 0.766 | 0.705 | 0.719 | 0.429 |
Extraction method: Principal component analysis; Rotation method: Promax with Kaiser normalisation.
From
Factor 5 also scores above 2.9 out of 4, indicating that it is an important motive for going to the specific beach. This factor contains two aspects, namely the Blue Flag status of the beach and its popularity. Factor 1 contains statements on the swimming experience that the beach offers. It has a mean score of 2.746 out of 4. The least important motive for visiting the specific beach is factor 4, has a mean score of 2.634 out of 4 and the statements contained in this factor incorporate the activities offered by the beach.
To explore the uniqueness of each cluster, the various socio-demographic, travel behavioural and motivational components (defined above) for each cluster were summarised by using means (for scale data) and frequencies (for categorical data). These are summarised in
Cluster descriptives and differences.
Variable | Cluster 1: Local low spenders | Cluster 2: Mixed high spenders | Cluster 3: Foreigners | Cluster 4: South Africans |
---|---|---|---|---|
Female | 57.00% (predominantly female) | 48.40% (predominantly male) | 59.20% (predominantly female) | 58.60% (predominantly female) |
Average age | 35.21 years | 31.9 years | 32.05 years |
35.29 years |
Language | English – 65.6%; Afrikaans – 21.6% |
English – 41.9%; Other – 58.1% |
English – 34.1%; Other – 65.9% |
English – 52.0%; Afrikaans – 33.7% |
Education | Diploma/Degree – 34.4%; Matric – 24.0% |
Post-graduate – 31.0%; Diploma/Degree – 27.6% |
Diploma/Degree – 41.3%; Post-graduate – 22.3% |
Diploma/Degree – 29.8%; Matric – 26.9% |
Province/Country | 43.8% WC; 32.0% KZN; 22.7% EC |
83.9% Non-RSA; 9.7% WC |
100% Non-RSA |
33.3% Gauteng; 23.7% WC; 16.9% EC |
Marital status | 5.4% Single; 43.3% Married |
64.5% Single; 25.8% Married |
63.9% Single; 32.2% Married |
46.6% Single; 41.4% Married |
Occupation | 66.4% Paid worker; 19.5% Student | 41.4% Paid worker; 41.4% Student | 67.4% Paid worker; 22.7% Student | 66.7% Paid worker; 16.5% Student |
Blue Flag beach | 68.80% |
90.30% |
96.70% |
70.30% |
Average group size | 3.68 | 2.96 | 3.51 | 3.80 |
Average number of nights spent | n/a |
75 | 13.62 | 9.73 |
Reason for beach visit | Sunbathing – 51.6% |
Sunbathing – 77.4% |
Sunbathing – 80.4% |
Sunbathing – 49.4% |
Three most important things a beach needs | Cleanliness – 38.3%; Good conditions – 35.2% |
Good conditions – 41.9% |
Good conditions – 42.4% |
Cleanliness – 36.9%; Safety – 29.7% |
Three most important risks at a beach | Theft – 42.2% |
Theft – 29.0% |
Theft – 25.0% |
Theft – 39.8% |
Average number of visits in summer | 52.67 |
19.41 | 3.94 | 15.97 |
Average number of visits in winter | 23.42 |
8.10 | 0.83 | 7.33 |
Motive 1 | 0.39 |
−0.17 | −0.41 |
0.12 |
Motive 2 | −0.12 | 0.02 | 0.11 | −0.02 |
Motive 3 | 0.06 | 0.16 | −0.11 | 0.03 |
Motive 4 | −0.03 | −0.32 | 0.07 | 0.00 |
Motive 5 | −0.05 | 0.09 | −0.16 | 0.13 |
Motive 6 | 0.08 | 0.19 | 0.00 | −0.05 |
, Significant at 10% level;
, significant at 5% level;
, significant at 1% level.
In terms of socio-demographic variables, there is no significant difference in the gender of each cluster, although there is a significant difference in their home language (
In terms of their travel behaviour, it is noteworthy that there are no significant differences in the average group size of the clusters. However, since members of cluster 1 are locals and stay in the beach vicinity, they record no nights spent at the beach, while cluster 2 shows an average of more than two months spent at the destination. These differences are significant. Confirming these significant differences are the times that beaches are visited annually in winter and summertime, with cluster 1 visiting beaches more often than any other cluster and members of cluster 3 visiting beaches the least.
The activities that each member takes part in at a beach also differ significantly between clusters. Clusters 2 and 3 tend to visit beaches to sunbathe (
What external factors do the different clusters look for when choosing a beach? This open-ended question was coded according to the responses and four key aspects were mentioned most frequently, namely a clean beach, a safe beach, good beach conditions (waves, water, sand and weather) and a beach that is not crowded. For all the clusters, a clean beach is a priority. Clusters 2 and 3 are more concerned with the conditions on the beach than the other 2 clusters (
The main risks that beachgoers perceive to be facing at beaches in South Africa are theft, sunburn, drownings and sharks. Although theft is the most important risk for all clusters, the percentage of respondents who identified it as the main risk differs significantly between clusters (
Finally, in terms of the motives for travelling to beach destinations, there are significant differences between clusters for motives 1 and 5. Cluster 1 values the swimming conditions at the beach (motive 1) significantly higher (
From the results of the analysis, the following findings are eminent:
The first and most important finding from this research is that there are different markets that visit beaches, not only in South Africa, but also worldwide, and these markets exhibit different characteristics and spending behaviour. For South African beaches, four key markets or clusters of beachgoers have been identified that vary in terms of their spending behaviour; they encompass clusters from low spenders to very high spenders, labelled as
The implication of this finding is that beach managers can use this information in order to focus on the preferences and needs of the high-spending markets. In the South African context, the highest-spending market consists not only of foreigners, but also of South Africans who stay in the vicinity of the beach. Keeping their loyalty and addressing their needs should therefore be a priority for beach managers.
Secondly, this research reveals that there are more behavioural variables and external beach factors influencing spending than socio-demographic variables – a result also found by Saayman and Saayman (
In comparison with the literature review and the impact that socio-demographic variables have on spending behaviour, the results contradict findings by Craggs and Schofield (
In comparison with the literature’s findings on behavioural variables that influence spending, we found that there are significant differences between the high- and low-spending clusters on a number of aspects. First, members of the lowest-spending cluster, which consists exclusively of locals living in the vicinity of the beach, visit the beach significantly more often in both seasons than any of the other clusters’ members. This is contradictory to most research on spending at events, where more frequent visitors tend to spend more (see Botha et al.
Members of the two higher-spending markets, namely the mixed high spenders and the foreigners, visit the beach mainly for sunbathing purposes, much more than any other market; for the mixed high spenders’ connectivity to the beach (Motive 3) and accessibility (Motive 6) are the most important reasons for visiting the beach. Concerning the foreigners, their most important motives for visiting the beach are beach quality (Motive 2) and beach activities (Motive 4). They tend to be less interested in typical South African beach activities such as swimming, sporting events and surfing, although relaxing is a more important reason for beach visits. Although members of the mixed cluster spend more time during both seasons visiting the beach, members of the foreign cluster do not necessarily visit South Africa for its beaches – they are the so-called unintentionally motivated tourists (Saayman
Members of the lower-spending South African cluster (cluster 4) typically visit the beach over summer holiday times (i.e. December and March) with family and friends. They also use the beach more for swimming and other sporting activities, similar to the low-spending local cluster. It is therefore not surprising that drownings at beaches (as an external factor) are a key concern for this, but no other, market, with drownings due to rip currents and rogue waves a regular occurrence.
Lastly, this research confirms the importance of environmental awareness and the impact thereof on beachgoers and spending, as was highlighted for the first time by Saayman and Saayman in 2012. For all four clusters, the cleanliness of the beach was identified as one of the three most important aspects when choosing a beach. This is further confirmed by the motives, since motive 2 measured the quality of the beach in terms of aspects such as cleanliness. It is the motive with the second-highest mean value and when the different clusters are compared, this motive scored high for all clusters except for locals (cluster 1). In addition, it is noteworthy that the two highest-spending clusters – mixed high spenders and foreigners – both prefer to go to Blue Flag beaches (90.3% and 96.7%, respectively). Motive 5 included Blue Flag as a motive for beach choice and the South African market scored significantly higher than any other cluster in terms of this motive. Cluster 2 scored the second highest.
This finding holds particular implications for all beach destinations worldwide. Beachgoers are becoming increasingly concerned with their environment; beaches that experience degradation or high levels of pollution will stand to lose, especially from the higher-spending segments of their market. Beach quality, water quality and a pristine beach environment need to be enhanced to ensure the survival of beach destinations.
Beaches are an essential part of many destinations’ tourism offering. Given the importance thereof, it is surprising that very little research has been conducted on understanding the spending behaviour and profiling of beach tourists. Therefore, the purpose of this article was to apply expenditure-based segmentation to beach tourists visiting South African beaches with the aim to distinguish between various segments and their characteristics.
In summary, this research identified four segments of visitors to South African beaches, ranging from low-spending locals to high-spending, mixed-market beachgoers. There are distinct differences between the segments and besides some socio-demographic differences, their beach-going behaviour and motives for visiting the beach also differ. The South African beachgoer is typically a lower spender, using the beach for activities and sport, whereas the high-spending segments are mainly foreigners who prefer the beach for sunbathing and relaxation. Important to all segments are the environmental qualities of the beach in terms of both cleanliness and beach safety. To the high-spending segments, Blue Flag status of the beach is also much more important, pointing to the possible negative effects of beach pollution for beach destinations’ sustainability.
South Africa is a country blessed with an ample supply of sandy beaches, making beach tourism one of the most lucrative tourism segments. However, for beach tourism to be sustainable, it requires that businesses (economy), the community and the environment should benefit. Although this article mainly focussed on the economic aspects, that is, what distinguishes higher-spending markets from lower spending markets, the results show that this cannot be separated from the environment. Well-managed beaches, where the environment is protected, attract higher-spending tourists, making it possible for the economies surrounding these beaches to thrive.
Based on the results, the article makes the following contributions: Firstly, it shows that not all beachgoers are the same – some are higher spenders than others and in terms of sustainability, it is important to attract the high spenders to the beach as well and to cater for their needs. Secondly, beach quality and the Blue Flag status of the beach are the most important motives for all beachgoers to South African beaches. Thirdly, the research shows that the two main threats to beach tourism and destinations, not only in South Africa, but globally, are (1) pollution of the oceans, and (2) global warming. Clean beaches with quality water attract tourists and the increased pollution and degradation of the world’s oceans and seas pose a major threat to the sustainability of beach destinations. In addition, the risk of sunburn is paramount among beachgoers; therefore, increased temperatures due to global warming also signal risks to beach destinations, which will have to think creatively in addressing these concerns.
Future research should be conducted to (1) determine the impact of global warming on beach tourism; (2) identify management strategies to address beach and ocean pollution; and (3) determine the economic contribution of beach tourism to economies and therefore the potential loss in income and employment due to adverse events.
The authors would also like to thank the reviewers for the constructive comments that helped shape the article.
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
The article was conceptualised and executed by both authors.
This work is based on research supported by the National Research Foundation of South Africa under Grant: UID 85625.
The Grant holder (NRF) acknowledges that opinions, Findings and conclusions or recommendations are that of the author and that the NRF accepts no liability whatsoever in this regard.
Summary statistics of the sample.
Variable | Minimum | Maximum | Mean | Standard deviation | |
---|---|---|---|---|---|
Age | 551 | 14 | 78 | 34.05 | 12.90 |
Travel group size | 532 | 1 | 30 | 3.63 | 3.64 |
Nights spent at the location | 433 | 0 | 365 | 14.09 | 36.74 |
Spending per person | 598 | 0 | 115 000 | 7 075.91 | 12 981.20 |
Number of summer visits | 522 | 0 | 270 | 21.05 | 54.63 |
Number of winter visits | 412 | 0 | 243 | 10.05 | 26.77 |
Frequency description of the sample.
Variables | Characteristics | Frequency | Per cent |
---|---|---|---|
Gender | Male | 240 | 40.885 |
Female | 347 | 59.114 | |
Language | English | 290 | 49.152 |
Afrikaans | 110 | 18.644 | |
Other | 190 | 32.203 | |
Education | No school | 10 | 1.721 |
Still in School | 29 | 4.991 | |
Matric | 118 | 20.309 | |
Diploma/Degree | 202 | 34.767 | |
Post-graduate | 119 | 20.481 | |
Professional | 86 | 14.802 | |
Other | 17 | 2.925 | |
Marital status | Single | 320 | 53.691 |
Married/Partner | 228 | 38.255 | |
Divorced | 21 | 3.523 | |
Widow/er | 9 | 1.510 | |
Other | 18 | 3.020 | |
Occupation | Student | 119 | 20.272 |
Paid worker | 383 | 65.247 | |
Unpaid worker | 16 | 2.725 | |
Unemployed | 32 | 5.451 | |
Retired | 37 | 6.303 | |
Local resident | No | 451 | 76.182 |
Yes | 141 | 23.817 | |
Origin | Foreigner (non-RSA) | 211 | 35.284 |
Western Cape (WC) | 120 | 20.066 | |
Gauteng | 87 | 14.548 | |
Eastern Cape (EC) | 73 | 12.207 | |
Free State (FS) | 9 | 1.505 | |
Kwazulu-Natal (KZN) | 64 | 10.702 | |
Mpumalanga (MP) | 8 | 1.337 | |
Northern Cape (NC) | 6 | 1.003 | |
North-West (NWP) | 12 | 2.006 | |
Limpopo (LP) | 8 | 1.337 |
Summary of analysis of variance results.
Variable | Sig. | |
---|---|---|
Age | 2.663 | 0.047 |
Group | 0.529 | 0.663 |
Nights | 30.284 | 0.000 |
Summer visits | 21.283 | 0.000 |
Winter visits | 14.934 | 0.000 |
Motivation 1 | 23.747 | 0.000 |
Motivation 2 | 1.540 | 0.203 |
Motivation 3 | 1.365 | 0.252 |
Motivation 4 | 1.592 | 0.190 |
Motivation 5 | 3.748 | 0.011 |
Motivation 6 | 1.044 | 0.373 |
Summary of chi-squared and Cramer’s V test results.
Variables | Chi-square |
Cramer’s V |
||
---|---|---|---|---|
Value | Sign | Value | Sign | |
Blue Flag | 57.242 | 0.000 | 0.311 | 0.000 |
Gender | 1.740 | 0.628 | 0.055 | 0.628 |
Sunbathing | 51.599 | 0.000 | 0.295 | 0.000 |
Swimming | 35.188 | 0.000 | 0.244 | 0.000 |
Surfing | 15.585 | 0.001 | 0.162 | 0.001 |
Walking | 33.275 | 0.000 | 0.237 | 0.000 |
Sport | 8.738 | 0.033 | 0.121 | 0.033 |
Fishing | 15.502 | 0.001 | 0.162 | 0.001 |
Relaxation | 7.199 | 0.066 | 0.110 | 0.066 |
Need_cleanliness | 4.238 | 0.237 | 0.085 | 0.237 |
Need_safety | 20.722 | 0.000 | 0.187 | 0.000 |
Need_conditions | 16.777 | 0.001 | 0.168 | 0.001 |
Need_activities | 5.741 | 0.125 | 0.098 | 0.125 |
Need_non-crowding | 10.454 | 0.015 | 0.133 | 0.015 |
Need_child friendliness | 5.080 | 0.166 | 0.093 | 0.166 |
Need_amenities | 3.370 | 0.338 | 0.075 | 0.338 |
Need_people | 3.222 | 0.359 | 0.074 | 0.359 |
Need_access | 2.796 | 0.424 | 0.069 | 0.424 |
Risk_crowding | 1.984 | 0.576 | 0.058 | 0.576 |
Risk_theft | 13.929 | 0.003 | 0.153 | 0.003 |
Risk_injury | 1.897 | 0.594 | 0.057 | 0.594 |
Risk_sun | 21.901 | 0.000 | 0.192 | 0.000 |
Risk_drowning | 6.740 | 0.081 | 0.107 | 0.081 |
Risk_sharks | 5.716 | 0.126 | 0.098 | 0.126 |
Risk_conditions | 10.369 | 0.016 | 0.132 | 0.016 |
Risk_safety | 17.393 | 0.001 | 0.171 | 0.001 |