Clustering Kruger National Park visitors based on interpretation

Botha, E.; Saayman, M.; Kruger, M. Article Clustering Kruger National Park visitors based on interpretation South African Journal of Business Management Provided in Cooperation with: University of Stellenbosch Business School (USB), Bellville, South Africa Suggested Citation: Botha, E.; Saayman, M.; Kruger, M. (2016) : Clustering Kruger National Park visitors based on interpretation, South African Journal of Business Management, ISSN 2078-5976, African Online Scientific Information Systems (AOSIS), Cape Town, Vol. 47, Iss. 2, pp. 75-88, https://doi.org/10.4102/sajbm.v47i2.62 This Version is available at: https://hdl.handle.net/10419/218610 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ S.Afr.J.Bus.Manage.2016,47(2) 75 Clustering Kruger National Park visitors based on interpretation E. Bothaa*; M. Saaymanb and M. Krugerb a Department of Entrepreneurship, Supply Chain, Transport, Tourism and Logistics Management; School for Public and Operations Management; University of South Africa; P O Box 392; Muckleneuk Ridge; Pretoria; 0003; South Africa b Tourism Research in Economic, Environs and Society; Faculty of Economic and Management Sciences; North West University; Potchefstroom; South Africa *To whom all correspondence should be addressed [email protected] Interpretation is considered to be an important educational tool that not only addresses visitors’ expectations but also contributes to national parks’ conservation purposes. This study segmented the Kruger National Park’s visitors based on expected interpretation services and revealed four clusters that differed based on their expected and experienced interpretation services as well as their motivational aspects. This study’s distinct contribution is the alternative segmentation approach which revealed the viability of the expected interpretation variable to use for ecotourism segmentation purposes. This study not only assists the Kruger National Park to appropriately address interpretation services but also aids other ecotourism destinations. Introduction National parks’ main resource of attraction is the environment (Said, Jaddil & Ayob, 2009) and eco-tourists to national parks are interested in learning about and experiencing the environment (Jurdana, 2009; Kang & Gretzel, 2012; Shultis & Way, 2006). As a result, ecotourism destinations such as national parks include an interpretation experience (Kara, Deniz, Kilicaslan & Polat, 2011) as part of ecotourism management to address the expectations of visitors (Saayman, 2009). It is therefore not surprising that Ham and Weiler (2007) found that interpretation influenced visitors’ park experience more positively than noninterpretation services like accommodation or restroom facilities. Consequently, interpretation aids in attracting more visitors, increasing sales (Ham, Housego & Weiler, 2005) and subsequently leads to higher revenue (Eagles, 2014). Interpretation can thus address park budget constraints as it is considered to be a successful park management technique (Reisinger & Steiner, 2006; Wearing & Neil, 2009). To plan for interpretation it is imperative to understand the profile of the visitors since decisions for interpretation development are made from the visitor’s point of view (Ham et al., 2005). Tourism marketing literature argues that people differ and can therefore be regarded as different markets with different needs (Dolnicar, 2008) that motivate certain behaviour (Getz, 2013). Hence the importance of market segmentation to identify profiles of visitors. One aspect of visitor profiles are the socio-demographic characteristics of the visitors and determine how the interpretation should be organised (i.e. the theme and type of media to communicate the theme) (Frauman, 2010) or adapted on the spot (Rabotić, 2010). Socio-demographic characteristics not only can assist in the development of interpretation but may also in turn explain the way tourists experience and regard satisfaction with services (Ham & Weiler, 2007). An interpretation programme developed according to the visitors’ profile in turn fulfils visitors’ expectations, influences tourist satisfaction and future visitation behaviour (Lee, 2009), increases the possibility of spending more time at the destination (De Rojas & Camarero, 2008) and thus increases the revenue needed for conservation which can also assist in greater sustainability. The Kruger National Park, which is the focus of this study, is the largest national park of the 21 national parks managed by SANParks and is considered to be their flagship park. However, even though Engelbrecht, Kruger and Saayman (2014) found that there is a gap between what the visitors to the Kruger National Park expected and experienced with regards to interpretation and education activities (part of interpretation), the focus on interpretation is less important in the strategic plan for commercialisation, which forms a significant part of their ecotourism pillar (SANParks, 2013), aiming to deal with budget constraints and continual financial sustainability (SANParks, 2013, 2014). In reality, interpretation is not a focal point for SANParks whatsoever but seems to be the initiative of different park managers under SANParks. Planning for interpretation is therefore not considered to be a priority in SANParks and especially not in the Kruger National Park. The aim of this study is therefore to identify market segments of the Kruger National Park based on expected interpretation services and then to differentiate the segments based on socio-demographic and behavioural characteristics, expected and experienced interpretation services as well as motivations to visit the park. Furthermore this study also has a unique contribution by identifying differences between the segments based on their experience with the interpretation services as 76 S.Afr.J.Bus.Manage.2016,47(2) to consequently reduce the gap between what interpretation services will be and what they should be (Ham & Weiler, 2006). This is because tourists’ needs, expectations and interests are directly related to the quality of the interpretation and an indication of a national park’s competitiveness (Rabotić, 2010). Literature review Planning for interpretation involves eight steps: (1) interpretive inventory; (2) interpretive goals; (3) identify visitors; (4) determine outcomes of goals; (5) develop themes; (6) develop media matrices; (7) implementation plan; and (8) evaluation process (Ham et al., 2005). To understand this process, the following sections will focus on what is meant by interpretation and specifically consider the process involved for identifying visitors (i.e. market segmentation) for interpretation and literature on segmentation within interpretation. Interpretation For the purpose of this study the definition stated by Tilden (1977), the father of interpretation, will be used. He defined interpretation as “an educational activity which aims to reveal meanings and relationships through the use of original objects, by first hand experiences, and illustrative media, rather than simply to communicate factual information” (Tilden, 1977:8). Tilden (1977) was consequently the first author to identify different types of interpretation, namely attended (i.e. person-to-person contact like game drives or educational talks) and unattended (no personal contact like educational displays and exhibits). Tilden’s classification closely corresponds with Ward and Wilkinson’s (2006) personal and impersonal interpretation. Other authors, however, have given more complex classifications like Kuo (2002) and Stewart, Hayward, Devlin and Kirby (1998). Stewart et al. (1998) classified interpretation into primary- (readably identified as interpretation like interpretation centres), secondaryand tertiary interpretation (which both respectively have an impact on the experience with primary interpretation like written commentaries at an activity and advertisement of interpretation). Stewart et al.’s (1998) distinction correlates well with Kuo’s (2002) classification of hard (i.e. secondary and tertiary interpretation) and soft interpretation (i.e. primary interpretation). Irrespective of which classification to use, interpretation is specifically used for successful and sustainable management of eco-tourism destinations such as national parks (Jurdana, 2009; Wearing & Neil, 2009). These interpretation services in national parks are usually in the form of game drives, guided walks or educational talks to name but a few. The reason that interpretation is necessary for effective park management is that interpretation can be regarded as the link between conservation and tourism management of a national park as indicated in Figure 1. Figure 1: Interpretation’s link between the tourism and conservation functions of a national park Source: Author’s own figure based on the literature review The main objective of national parks in South Africa is to conserve the environment to be retained in its natural state (National Parks Act 57 of 1976). Interpretation, as depicted in Figure 1, assists with conservation management by broadening visitors’ knowledge about the place they are visiting by revealing the significance of their experience and assisting their understanding (Periera, 2005; Reisinger & Steiner, 2006). It is thus not surprising that interpretation is regarded as part of the ecotourism experience (Kara et al., 2011). This, in turn, leads to the protection of the environment since the understanding creates a respect and concern for species (Ballantyne, Packer & Sutherland, 2011) supporting nature conservation work and protecting endangered species (Zeppel & Muloin, 2008). Understanding the conservation philosophy of national parks therefore determines the overall direction of the interpretation services to offer (Ward & Wilkinson, 2006). As illustrated in Figure 1, interpretation is also a means of managing visitors’ educational expectations (Saayman, 2009) since visitors to national parks are well educated and expect information-rich experiences (Jurdana, 2009). Interpretation as a result adds value to the tourism experience (Ballantyne et al., 2011; Ham & Weiler, 2006) and leads to a range of other benefits such as increased satisfaction, loyalty, increased purchasing, increased revenue, visitors spending more time at the national park, encouraging other visitors to visit the park, and providing positive word-of-mouth referrals for the park (De Rojas & Camarero, 2008; Engelbrecht et al., 2014; Hwang, Lee & Chen., 2005; Lee, 2009; Zeppel & Muloin, 2008). National parks, and especially the Kruger National Park, should therefore place more emphasis on delivering interpretation services given that interpretation will not only benefit the park’s tourism function but the additional revenue (and other benefits) will also enable the park to fulfil its main purpose of conserving the environment. However Ham et al. (2005) caution that the visitor is a critical element to the delivery and outcomes of interpretation. Kuo (2002) suggests that, at the planning stage for an effective interpretation programme, management needs to research visitor demographics since each visitor has unique interests, needs, expectations, preferences and purpose for visiting. For that reason, it is important to take the time and effort to achieve a clear understanding of who the interpretation visitors are, why they visit, and the experiences they seek and enjoy (Ham et al., 2005). Hence the importance of market segmentation based on interpretation expectations. S.Afr.J.Bus.Manage.2016,47(2) 77 Market segmentation based on interpretation as a segmentation variable Market segmentation is a process of dividing a market into distinct subset of customers with common needs and/or characteristics (Schiffman & Kanuk, 2004). Segmenting a market is, however, only the first step of a three-phase marketing strategy. After segmenting a market into different segments, one or more target markets (step two) are identified and a specific marketing mix for each target market is designed where after the product is positioned for each of these target markets (Dolnicar, Grün, Leisch & Schmidt, 2014). Profiling customers is therefore crucial for policymakers, managers and marketing analysts since identifying homogeneous markets is an essential step for both planning and developing strategies (D’Urso, De Giovanni, Disegna & Massari, 2013). Focusing only on one or two target markets increases the chances of marketing success and improves the overall survival and profitability of the business (Dolnicar, Kaiser, Lazarevski & Leisch, 2012). Segmentation can be conducted using various different bases such as demographics (e.g. age, gender, marital status); sociodemographics (e.g. education, household composition); socio-economics (e.g. income, employment); socio-cultural (e.g. family life cycle, social class); geographical (e.g. place of origin); psychological (e.g. personality, perceptions, attitudes); psychographics (e.g. activities-interests-opinions, beliefs, values, lifestyles); benefits (e.g. what consumers want, motivations to travel); product-related (e.g. special interest travellers); and user-related (e.g. rate of usage, awareness, brand loyalty) (Getz, 2013; Morrison, 2013). The basic premise of market segmentation, depending on the circumstances, is that some customers (i.e. segments) are similar to each other and, in turn, different from other customers (i.e. segments). It is therefore possible to segment a market into different groups based on only one criterion or a combination, to delimit target markets (Getz, 2013; Morrison, 2013). The segmentation process therefore involves selecting a variable or a combination of variables from the segment bases mentioned earlier that best differentiate between customers (Nykiel, 2007). This variable(s) is/are known as the active variable(s) (Dillon & Mukerjee, 2006). Active and other non-active variables can then further be used to characterise or differentiate between the segments (Dillon & Mukerjee, 2006; D’Urso et al., 2013) and appropriately profile customers. This study makes use of cluster analysis, which will be discussed at a later stage, to determine clusters (i.e. segments) for the Kruger National Park based on their expectations regarding interpretation services. One of the criticisms that researchers should address for cluster analysis is the selection of active variables. This is because (i) the technique has no means of differentiating relevant from irrelevant variables and (ii) that the cluster solution is dependent upon the variables (Hair, Black, Babin & Anderson, 2010). A strong conceptual support is therefore needed and researchers are recommended to select the active variables with the research goal as criterion (Hair et al., 2010). Hence it is necessary to consult previous interpretation research to identify relevant segmentation variables. Upon consulting previous research Ward and Wilkinson (2006) explain that both socio-demographic and behavioural characteristics are useful in planning for interpretation. Socio-demographic characteristics help with deciding which facilities, programmes, topics and recreational opportunities should be provided and motivations (i.e. behavioural characteristics) are useful in preparing programmes that meet and satisfy visitors’ expectations (Ward & Wilkinson, 2006). Segmenting interpretation markets by identifying their age gives destination managers an idea of prior knowledge (e.g. older individuals might have greater prior knowledge than younger individuals) (Peake, Innes & Dyer, 2009) and attitudes towards animals in captivity (e.g. older individuals might know more about conservation and have more affection towards certain conservation aspects) (Lucas & Ross, 2005) for developing interpretation programmes. Determining visitors’ education level, on the other hand, may also assist in developing interpretation to influence their attitudes toward conservation (Lucas & Ross, 2005). Other variables or visitor characteristics like language or nationality determine the language in which interpretation should be presented (Saipradist & Staiff, 2007) or how the interpretation will be consumed (Prentice & Anderson, 2007). If visitors are motivated to spend time with family and friends the interpretation programme can be designed to capitalise on this by evoking powerful memories and making lasting impressions (Ballantyne et al., 2011). Determining their interpretation expectations or motivations allows managers to cater for their skill and knowledge (Eagles, 2004) or making improvements to interpretation services (Lee, Jeon & Kim, 2011). Particularly interesting from the table above is that little research has been conducted on market segmentation within the interpretation context except for Chen, Hwang and Lee (2006). The rest of the studies only identified certain differences between interpretation and one or more socio-demographic and behavioural characteristic but the aim was not to identify market segments for interpretation or use it as a segmentation base. Even though little research refers to product-related segmentation for interpretation, it is a credible segmentation base for segmenting ecotourists. According to Getz (2013) and Morrison (2013) product-related segmentation refers to segmenting visitors based on specific interests. One of the special interest tourism categories is ecotourism of which the visitors (i.e. ecotourists) are characterised as highly educated and interested in learning about the environment (Jurdana, 2009; Kang & Gretzel, 2012; Shultis & Way, 2006) and motivated to visit ecotourism destinations to escape their daily lives (Chan & Baum, 2007). In view of the latter and considering that national parks deliver interpretation services (Kara et al., 2011) to address the expectations of visitors (Saayman, 2009), segmenting visitors based on their expectations of interpretation services is a convincing active variable. To date however, little research has attempted to segment visitors within the interpretation context. Research also falls short in using expected interpretation as a variable for product-related (i.e. ecotourism) segmentation. This 78 S.Afr.J.Bus.Manage.2016,47(2) study will therefore be the first of its kind in addressing both the gaps of segmentation within interpretation as well as using expected interpretation variables to segment markets. Interpretation assists in two ways: (i) interpretation allows visitors to understand and appreciate the environment they are visiting and hence assist conservation, and (ii) interpretation is part of addressing tourists’ expectations of learning and thus supports the park’s tourism function and, in turn, increases the revenue for conservation. To achieve these goals, planning for interpretation necessitates that the Kruger National Park should segment the market. Segmentation can specifically be done on the basis of visitors’ expectations for interpretation as a type of product-related segmentation base. This will specifically identify possible markets for interpretation services where non-active variables can be used to further profile visitors and position interpretation services accordingly. Method of research The method of research conducted in this study is discussed under the following headings: (i) survey design; (ii) survey implementation; and (iii) statistical analysis. Study design This research followed a quantitative research approach by means of a self-administered questionnaire to collect data from the visitors to the Kruger National Park. Sections A to E were predominantly used for the purpose of this study by differentiating between interpretation market segments based on socio-demographic, behavioural and motivational variables. Section A pertained to demographic characteristic questions (mostly nominal and ordinal) of respondents in the Kruger National Park. Section B’s questions related to expected and experienced interpretation services as well as behaviour intentions as a result of the interpretation services in the Kruger National Park. These questions were measured on Likert scales that complied with the sensitive aspect of good measurement (Zikmund, Babin, Carr & Griffin, 2010) since: (i) the expected questions measured respondents’ importance of the listed interpretation services (see Table 1); (ii) the experienced questions measured how well the interpretation services was experienced (see Table 1); (iii) the behavioural intentions questions measured respondents’ level of agreement with the listed behavioural intentions (see Table 4). To cover the breadth of the interpretation domain, the above questions also complied with content validity (Malhotra, 2007; Zikmund et al., 2010) since the questions were based on the following authors’ work: Ballantyne, Packer and Hughes (2008); Ballantyne et al. (2011); De Rojas and Camarero (2008); Frauman and Norman (2004); Ham and Weiler (2007); Henker and Brown (2011); Hwang et al. (2005); Kuo (2002); Lee (2009); Lee and Balchin (1995); Lee, Lee, Kim and Mjelde (2010); Madin and Fenton (2004); Mitsche, Reino, Knox and Bauernfeind (2008); Orams (1994;1996); Periera (2005); Powell and Ham (2008); Reisinger and Steiner (2006); Stewart et al. (1998); Ward and Wilkinson (2006); and Zeppel and Muloin (2008). By means of factor analysis (see Results), this study addresses construct validity by determining the classification of interpretation that are most consistent with the variables of Section B (Zikmund et al., 2010). The last aspect, reliability, was measured by means of the alpha coefficient: when α < 0.6 the scale is unreliable and indicates unsatisfactory internal consistency (Malhotra, 2007); α = 0.6 and 0.7 indicates fair reliability; α = 0.7 to 0.8 indicates good reliability; and α = 0.8 to 0.95 indicates very good reliability (Zikmund et al., 2010). Along with Section A, sections C to E were used to compare with section B’s continuous variables. These sections captured information regarding respondents’ spending habits, where they heard about the park, motivations to visit the park, preference of the Big 5 animals, and experience with noninterpretation services in the park. These questions included Likert scale, open ended as well as close ended categorical questions. Sampling and survey implementation The distribution of questionnaires was done in two phases to cover the whole park. Phase one (between 27 December 2011 and 3 January 2012 in the southern region) covered Satara, Skukuza, Lower Sabie, and Berg en Dal rest camps. Phase two (conducted in the northern region between 24 June and 2 July 2012) covered Olifants, Letaba, Mopani, Shingwedzi and Punda Maria rest camps. Fieldworkers were assigned for two days to a specific area within the rest camps and briefed beforehand on the goals and the content of the questionnaire. Fieldworkers distributed one questionnaire per overnight travelling group by explaining the purpose of the study to the potential respondent, voluntary participation and indicated that they may withdraw from the study at any moment. Since only one questionnaire should be distributed per travelling group, the total of overnight visitors to the Kruger National Park for 2012 was divided by the average of people per travelling group (4) to calculate the population for sampling purposes. This resulted in a new population of (N) 352 949 (SANParks, 2012). A sample of (n) 384 is required for a population (N) of 352 949 tourists to the Kruger National Park with a 95% confidence level and a 5% sampling error [d is in other words expressed as (.05)] to validate analysis (Krejcie & Morgan, 1970). After cleaning the data set (extreme outliers deleted) n = 687 and hence was more than the required number of questionnaires to validate analysis. Statistical analysis Once the questionnaires were obtained, data were captured in Microsoft Excel and analysed by means of SPSS (SPSS, 2013). The analysis was done in three stages as discussed below: Factor analyses were done in the first stage of the study to determine (i) the interpretation services that respondents S.Afr.J.Bus.Manage.2016,47(2) 79 expect from the Kruger National Park and to be used as one of the scenarios in cluster analysis (see second stage). Furthermore; factor analyses determined (ii) how respondents experienced these interpretation services at the Kruger National Park; and (iii) the motivations of respondents who visit the Kruger National Park that , along with the results of the expected interpretation factor analysis, the results of these factor analyses were used in stage three of data analysis to differentiate between different clusters. For all three of these factor analyses, the Bartlett’s (1954) test of sphericity (i.e. p ≤ .05) and Kaiser-Meyer-Olkin (KMO) (Kaiser, 1974) measure of sampling adequacy (i.e. is a minimum of 0.6) was performed to determine whether a factor analysis could have been conducted on the relevant scales’ data variables. To determine the smallest number of factors from the data variables, the pattern matrix of the principal axis factoring extraction technique was applied whereas the Kaiser Normalisation (eigenvalues above 1.0 or more) guided the decision on the number of factors retained. The decision of factor loadings for this study was based on the following guidelines: (i) factors are reliable when the average of the four largest loadings is greater than 0.60 or the three largest loadings are greater than 0.80 (Stevens, 2009); (ii) factors with only a few low loadings can be interpreted if the sample size is at least 300 to indicate a reliable factor (Guadagnoli & Velicer, 1988); and (iii) for a sample of 600, the loadings of variables should at least be 0.210 (Stevens, 2009). Where an item cross-loaded on two factors the item was categorised under the relevant factor guided by literature where the Oblimin oblique rotation technique assisted in factor interpretation. Only reliability coefficients above 0.6 were considered as acceptable for the study since coefficients below 0.6 indicate poor reliability of the scale and unsatisfactory internal consistency (Malhotra, 2007; Zikmund et al., 2010). Inter-item correlations were additionally calculated as another reliability measure which, recommended by Briggs and Cheek (1986), should be between 0.2 and 0.4. Secondly, cluster analysis was performed on several expected interpretation variables. The purpose of clustering methods is to maximise homogeneity of observations within a cluster or segment and simultaneously maximise heterogeneity between clusters or segments (Hair et al., 2010; Zikmund et al., 2010). As discussed earlier, the expected interpretation variables were chosen specifically to segment ecotourism visitors based on product-related segmentation. Since the selection of variables for cluster analysis is a crucial consideration, it was necessary to compare three different scenarios to select the correct set of variables for cluster analysis. These three scenarios were identified as: (1) raw data (all 24 expected interpretation variables) since Dolnicar and Grün (2008) recommend the use of raw data as the interpretation of segments in factor-clustering is based on transformed information and thus questionable; (2) factors (identified from the expected interpretation factor analysis, see Table 1) which is a typical procedure in the preprocessing of cluster analysis in tourism literature; and (3) surrogate variables that represent the original variables in a factor and interpreted in terms of the original factors (Hair et al., 2010; Malhotra, 2007) [variables with the highest loadings on each factor in the factor analysis (these variables were the least correlated) as well as the variable “Informed staff who can handle queries regarding the interpretation of the park” which was removed from the factor analysis and regarded as a factor on its own]. Outliers were deleted from the three scenarios’ datasets (Hair et al., 2010) and coefficient of variance (Cv) was calculated for all three scenarios to determine which scenario has the least variability. The variability along with the multicollinearity assumption guided the decision as to which scenario should be used for cluster analysis. It was therefore decided to use the surrogate variables since these variables were the least correlated, variable and addressed multicollinearity. The surrogate variables were then cluster analysed both hierarchically and non-hierarchically. Since the nonhierarchical procedure requires the researcher to indicate the number of clusters to be retained (Schmidt & Hollensen, 2006; Sharma & Kumar, 2006) the initial clustering solution from the hierarchical procedure was then specified in the nonhierarchical procedure (Hair et al., 2010; Malhotra, 2007). The hierarchical and non-hierarchical procedures typically used in tourism research (Füller & Matzler, 2008) are Ward’s [clustering technique aims at minimising inter-cluster variance (Malhotra, 2007; Schmidt & Hollensen, 2006)] and K-means clustering techniques [maximises within-cluster homogeneity (Hair et al., 2010) and identifies clusters of nearly equal sizes (Sharma & Kumar, 2006)]. The K-means clustering technique selects temporary k centres based on the number of clusters specified from the Ward’s clustering technique and partitions observations to those centres (Hair et al., 2010; Malhotra, 2007). The cluster solution was validated to assure the cluster solution is representative of the general population and generalisable to other objects (Hair et al., 2010) by running the analysis on sorted cases (in this case by Age) as the order of cases can affect the cluster membership (Hair et al., 2010:557; Malhotra, 2007). Only 18% of the second cluster analysis’ cases were not clustered together as they did with the first cluster analysis and thus indicate that the cluster solution is stable (Hair et al., 2010). Thirdly, both chi-square tests and ANOVAs were calculated to investigate whether any significant differences exist between the clusters based on active (i.e. the three surrogate variables) and non-active variables (i.e. variables not used for cluster analysis) like socio-demographic and behavioural characteristics, experienced interpretation and motivational aspects. Chi square tests were used to identify differences between two or more categorical variables (Pallant, 2011) and ANOVAs to investigate statistical differences of mean values between groups (Pallant, 2011). Tukey’s Honestly Significant Difference test was additionally used as a posthoc test (Pallant, 2011). The effect sizes were also calculated [phi (Φ) coefficient for chi square tests and d-value for ANOVAs] to determine whether marginal differences exist. According to Cohen (1988) when Φ-value or d-value is 0.2 it indicates a small effect (research ought to be replicated to determine whether there is an effect or if the result is practically non-significant); d = 0.5 indicates a medium effect (might point towards practical significance); and if d = 0.8 80 S.Afr.J.Bus.Manage.2016,47(2) shows practical significance (practical importance) (Steyn, 2000). Results The following section examines the results obtained from the factor analyses, cluster analyses, chi-square tests as well as the results from the ANOVAs. Results of the factor analyses As previously indicated, three factor analyses were conducted for this study: (i) expected interpretation services, (ii) experiences with the interpretation services of the park, and the (iii) motivations of respondents to visit the park. These factors were then used for further analyses to profile the market segments identified in the second and third stage of analyses. The discussions of the results follow in the sections below. Factor analyses on expectations and experiences with interpretation services The first round of factor analysis revealed that one variable, informed staff who can handle queries regarding the interpretation of the park did not load under the correct factor as literature indicates (i.e. primary interpretation) and it was therefore decided to exclude it from the second round of analysis and regard it as a factor on its own. The principal axis factoring analysis using an Oblimin oblique rotation with Kaiser normalisation on the remaining 23 variables identified two factors for expectations as well as experiences with interpretation services (see Table 2). Factor 1 was labelled Primary interpretation, Factor 2 Secondary interpretation and Factor 3, as previously indicated is Knowledgeable staff. The Bartlett’s test revealed statistical significance (p = 0.001) and the KMO for sampling adequacy resulted in 0.941 and 0.931 respectively. Only eigenvalues above 1 were used which resulted in two factors that accounted for 56% and 50% respectively of the total variance explained. The average of all items contributing to a specific factor revealed factor scores that interpret the factor to the original five-point Likert scales respectively. All factors indicated very good convergent validity with Cronbach alphas above 0.8 and inter-item correlations of between 0.40 and 0.50. Table 1 indicates that respondents regard primary interpretation as very important (2.42) for a quality experience in the Kruger National Park. Secondary interpretation (1.74) as well as knowledgeable staff (1.74) are also very important, but marginally less important compared to primary interpretation. The experience with these factors revealed that secondary interpretation (2.39) was experienced well; however primary interpretation (2.92) and knowledgeable staff (3.13) were experienced moderately. Comparing the expectations with the experiences with these factors disclose that only secondary interpretation met the respondents’ expectations (1.74) as they have indicated that they have experienced this factor satisfactorily (2.42) and that primary interpretation (2.42) and knowledgeable staff (1.74) however were not experienced according to the expectations of respondents since the mean values of the experienced scale reveal that both these factors were experienced moderately with 2.92 and 3.13 respectively. Factor analysis on motivations to visit the park A minimum of three factors were identified on the 12 motivation variables by the principal axis factoring analysis, using an Oblimin oblique rotation, with Kaiser normalisation and eigenvalues above 1 (see Table 2). Hence the total variance explained was 55%. Factor 1 was labelled Special interest needs since these items refer to interests that ecotourism literature reveals for these visitors, Factor 2 Escape and Factor 3 Park facilities and value. The KMO for sampling adequacy resulted in 0.766 and the Bartlett’s test revealed statistical significance (p = 0.001). The average of all items contributing to a specific factor revealed factor scores that interpret the factor to the original five-point Likert scale. All three factors indicated very good convergent validity with Cronbach alphas between 0.7 and 0.9 and interitem correlations of between 0.2 and 0.7. Respondents indicated that escape (4.26) is a very important motivation to visit the Kruger National Park. Even though park facilities and value (3.56) can be considered to be very important as well, this motivation differs marginally from special interest needs as an important (3.34) motivation to visit the park. S.Afr.J.Bus.Manage.2016,47(2) 81 Table 1: Factor analyses for expectations of – and experiences with interpretation services of the Kruger National Park Interpretation Expectations◦ Factor loading Interpretation Experience◦ Factor loading Factor 1: Primary interpretation Factor 1: Primary interpretation Interpretation activities e.g. slide shows, informative sessions and specialist talks .825 Geological and climatological displays .821 Auditorium with nature videos .780 Interpretation activities e.g. slide shows, informative sessions and specialist talks .817 Geological and climatological displays .716 Educational talks, activities and games for children .810 Educational displays .527 Educational displays .771 Educational talks, activities and games for children .513 Information boards regarding the fauna/flora in the park .668 Information regarding the history of the park .401 Information regarding the history of the park .656 Information boards regarding the fauna/flora in the park .388 Auditorium with nature videos .637 Lifelike examples of different animals, insects, birds and trees with descriptive data .366 Lifelike examples of different animals, insects, birds and trees with descriptive data .491 Information centres and interpretation centres in specific rest camps .264 Identification of trees, e.g. nameplates or information boards .425 Identification of trees, e.g. nameplates or information boards .234 Authenticity of interpretation .421 Authenticity of interpretation .825 Information centres and interpretation centres in specific rest camps .407 Interactive field guides on game drives and guided walks .780 Interactive field guides on game drives and guided walks .268 Mean value 2.42 Mean value 2.92 Reliability coefficient .91 Reliability coefficient .91 Average inter-item correlation .45 Average inter-item correlation .44 Factor 2: Secondary interpretation Factor 2: Secondary interpretation Clear directions to rest camps and picnic areas .964 Clear directions to rest camps and picnic areas .886 Available route maps with descriptive information .951 Accessibility of the park .829 Good layout of the park, rest camps and routes .900 Available route maps with descriptive information .813 Accessibility of the park .889 Good layout of the park, rest camps and routes .758 Enforcement of park rules and regulations .764 Available books, brochures, information pamphlets and park guides for animal, insects, birds and trees .643 Available books, brochures, information pamphlets and park guides for animal, insects, birds and trees .709 Information regarding interpretation in the park available on the web .457 Information regarding interpretation in the park available on the web .626 Lookout points in the park .450 Lookout points in the park .567 Information boards with animal tracking .429 Marketing of the park and its wildlife as well as activities on the web, in magazines, newspapers and on the radio .523 Marketing of the park and its wildlife as well as activities on the web, in magazines, newspapers and on the radio .385 Information boards with animal tracking .515 Enforcement of park rules and regulations .358 Bird hides in the park .324 Bird hides in the park .309 Mean value 1.74 Mean value 2.42 Reliability coefficient .91 Reliability coefficient .88 Average inter-item correlation .50 Average inter-item correlation .40 Factor 3: Knowledgeable staff Factor 3: Knowledgeable staff Informed staff who can handle any queries concerning the interpretation aspects in the park .647 Informed staff who can handle any queries concerning the interpretation aspects in the park .630 Mean value 1.74 Mean value 3.13 Total variance explained 55.6% Total variance explained 49.5% ◦Likert scales: Expectations: 1 = Extremely important to 5 = Not at all important; Experience: 1 = Excellent to 5 = Very poor 82 S.Afr.J.Bus.Manage.2016,47(2) Table 2: Results of the factor analysis on the motivations of respondents to visit the Kruger National Park Motivations◦ Factor loading Mean value Reliability coefficient Average inter-item correlation Factor 1: Special interest needs 3.34 .71 .26 Primarily for educational reasons .656 To explore a new destination .591 To spend time with friends .497 For the benefit of my children .405 To photograph animals and plants .349 It is a spiritual experience .295 To see the Big 5 .255 Factor 2: Escape 4.26 .83 .71 To relax .751 To get away from my routine .729 Factor 3: Park facilities and value 3.56 .72 .47 The park has great accommodation and facilities .750 I am loyal to the park .649 It is value for money .622 Total variance explained 54.8% ◦Likert scale: 1 = Not at all important to 5 = Extremely important Results of the cluster analyses Given that this study made use of a conjoint hierarchical and non-hierarchical approach, the surrogate variables were first analysed by means of Ward’s cluster analysis. The number of cluster solutions to pre-specify for the K-means cluster analysis was calculated by means of the percentage changes in heterogeneity from the coefficients obtained from the Ward’s cluster analysis. This rule specifies that when large increases in heterogeneity occur in moving from one stage to the next, the prior cluster solution is selected because the new combination is joining quite different clusters (Hair et al., 2010). The Ward’s clustering technique identified four clusters that should be retained for further K-means cluster analysis. The second round of cluster analysis thus revealed four cluster or segments of various sizes where segment 1 is n = 158, 2 is n = 429, 3 is n = 69 and 4 is n = 31. The differences between these segments are more clearly identifiable when ANOVAs are performed based on the active variable (i.e. expected surrogate interpretation variables) illustrated in Table 3. Table 3: ANOVAs for expected interpretation Characteristics Cluster solutions d (1-2) d (1-3) d (1-4) d (2-3) d (2-4) d (3-4) F-ratio Sig. level 1 Eager seekers (n = 158) 2 Inquisitive seekers (n = 429) 3 Comfort seekers (n = 69) 4 Quasiinterested seekers (n = 31) Mean Std. dev Mean Std. dev Mean Std. dev Mean Std. dev Primary 3.02 b .54 2.02 a .52 3.22 b .65 3.63 c .78 1.85*** .31* .78** 1.85*** 2.06*** .53** 233.229 .001# Secondary 1.77 b .37 1.53 a .45 2.14 c .49 3.91 d .85 .53** .76** 2.52*** 1.24*** 2.8*** 2.08*** 270.033 .001# Knowledgeable staff 1.52 a .50 1.39 a .54 3.38 b .67 4.29 c .82 .24* 2.78*** 3.38*** 2.97*** 3.54*** 1.11*** 469.766 .001# # indicates significant differences (p ≤ .05); *d = 0.2: small effect, ** d = 0.5: medium effect, *** d = 0.8: large effect ° Measure from 1 = Extremely important to 5 = Not at all important a differs from where b, c and d are indicated and vice versa