Customer segmentation for marinas: Evaluating marinas as destinations

Customer segmentation for marinas: Evaluating marinas as destinations

Tourism Management 56 (2016) 156e171 Contents lists available at ScienceDirect Tourism Management journal homepage: www.elsevier.com/locate/tourman ...

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Tourism Management 56 (2016) 156e171

Contents lists available at ScienceDirect

Tourism Management journal homepage: www.elsevier.com/locate/tourman

Customer segmentation for marinas: Evaluating marinas as destinations Neslihan Paker a, *, Ceren Altuntas¸ Vural b a b

Yas¸ar University, Department of Transportation Services, Universite Cad. No: 35-37 Agacli Yol Bornova 35100 Izmir, Turkey _ Dokuz Eylul University, Department of International Trade, Turabiye Mah., Beyler Cad. No:2 Seferihisar 35460 Izmir, Turkey

h i g h l i g h t s  Limited research exists on marina marketing.  Marinas should be evaluated as touristic destinations.  Pullepush framework is used to segment marinas' yachter customers.  Five segments are identified exhibiting different socio-demographic characteristics.  Results have implications for marina marketing and destination marketing research.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 October 2015 Received in revised form 5 February 2016 Accepted 20 March 2016

Marinas are a significant part of marine tourism activity and they are complex organizations having a highly heterogeneous business structure with many different companies trying to provide the various services that altogether compromise the so called “marina services”. From this perspective, they can be described as destinations and analyzed with a destination marketing perspective. This study aims to conduct a benefit segmentation approach to marinas as destinations, in order to identify the existing market segments based on yachters' expectations from them. Data were collected from 261 yachters of seven marinas located on Turkey's Aegean coast in 2014. The five identified segments are labelled as socially oriented, indifferent, supportive facilities oriented, service and prestige oriented, and touristic attractiveness oriented clusters. Clusters are validated by nine independent variables that define their socioedemographic characteristics and individual motivations for traveling to marinas. The results offer important implications both for practitioners and scholars. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Benefit segmentation Motivation factors Marinas Yachting Marine tourism Destination marketing

1. Introduction In today's markets where it is impossible to provide products that appeal to all available customers due to their highly heterogeneous needs and expectations, companies try to divide markets into groups of consumers carrying homogeneous characteristics depending on a previously identified set of variables (Kara & Kaynak, 1997). The same condition also prevails for tourism services and destination marketing decisions. Segmentation of travelers has been a widely covered area in destination marketing literature (e.g. ; Frochot, 2005; Mohsin, 2005; Park & Yoon, 2009; Prayag & Hosany, 2014; Prayag, Disegna, Cohen, & Yan, 2015). Within this literature, benefit segmentation approach has

* Corresponding author. E-mail addresses: [email protected] (N. Paker), [email protected], [email protected] (C.A. Vural). http://dx.doi.org/10.1016/j.tourman.2016.03.024 0261-5177/© 2016 Elsevier Ltd. All rights reserved.

gained a significant popularity, besides the widely accepted approaches to segmentation such as using demographic characteristics. Classifying travelers according to the benefits that they seek from their travel experience can be seen as more likely to predict segments that are valuable in providing information on travel behavior and destination choice (Botschen, Thelen, & Peiters, 1999). This is due to the belief that benefits that travelers seek in going to destinations are related with their motivations and this helps marketers to both segment their markets and profile their customers in a more accurate way (Frochot & Morrison, 2000). Although benefit segmentation has been widely covered in the tourism literature and applied across a variety of destinations and other situations (Frochot & Morrison, 2000), the application of the methodology for marina selection decisions by yachters is a rather neglected area. Marinas can be considered as destinations if a destination is defined as “a diverse and eclectic range of businesses and people, who might have a vested interest in the prosperity of their destination community” (Pike & Page, 2014: 203). They are a

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significant part of marine tourism activity and they are complex organizations involving a highly heterogeneous business structure with many different companies trying to provide the various services that altogether compromise the so called “marina services”. All these companies and organizations are working for the prosperity of their marina. Although marinas are a very important part of the economic activity in many countries and they contribute to the well-being of the related industries and their countries, there is a lack of scholarly interest in the comprehensive analysis of the industry (Raviv, Tarba & Weber, 2009). Sharing the considerations of Raviv et al., (2009), this study tries to make a contribution to scholarly research that can be used by marina managers for segmenting their yachter markets depending on their motivations. The study uses the pushepull framework for operationalizing the motivations of yachters in their marina selection decisions. Also the study makes an attempt to fill a void in the destination marketing literature by evaluating marinas with a destination perspective and applying benefit clustering methodology to these destinations. The study tries to respond Dolnicar and Ring (2014) call by trying to translate the findings of the study into operational marketing recommendations for marina managers. The benefit segmentation approach is strengthened by using socioedemographic criteria and several yachting characteristics in order to develop strategies that would enable marketers to communicate and reach each segment in an effective way (Hanlan, Fuller & Wilde, 2006). The study sets its research question based on these research aims and asks if there are different clusters based on benefits sought by yachters when they select the marinas to call or stay. The study is applied in Turkey's Aegean coast considering the potential of this region and the new marina investments being planned in the area. In addition to these, the study tries to explain the existing differences between customer segments in terms of demographics, yachting characteristics and also individual motivations for traveling to marinas. The rest of the paper is organized as follows. The second section reviews the literature on market segmentation in tourism studies, benefit segmentation approach in destination marketing and tries to conceptualize marinas as destinations. The third section explains the sampling, methodology and exhibits the findings of the analysis. The last section provides a discussion on the results together with practical and theoretical implications for further scholarly research. 2. Literature review 2.1. Market segmentation and studies on tourism services Market segmentation is a widely accepted strategic marketing tool by companies that possess scarce resources and that wish to allocate these resources effectively to reach their objectives (Assael & Roscoe, 1976). Since the early days of its practice, marketers have tried to segment markets geographically (Blattberg, Peacock & Sen, 1976) or based on descriptive characteristics such as being a buyer or a non-buyer, men or women, heavy user or rare user and such (Plummer, 1974). Descriptive characteristics are generally composed of geographic, demographic or psychographic variables and a second large group is composed of behavioral characteristics such as consumer responses to benefits, usage occasions or brands (Kotler Keller, 2012). The two approaches towards market segmentation are a priori and post-hoc approaches (Wind, 1978). A priori approach chooses some variables of interest and then classifies the market accordingly. However, this creates a risk where all the members of a specific market segment may not respond to a market stimulus in the same way. According to the literature,

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variables based on solely descriptive data especially on an a priori basis are evaluated as poor predictors of buying behavior (Dolnicar, 2002; Tan & Lo, 2008). Post-hoc or posteriori approach, on the other hand, collects data first depending on a selected set of interrelated variables and then tries to segment the markets into groups where within-group similarities are high and between-group similarities are low (Wind, 1978). Regardless of the method chosen for segmentation, consumer response to a market stimulus based on a given variable may not prevail for a long time or it may change depending on the analysis of different behavioral combinations (Assael Roscoe, 1976). Smith (1956) indicated that redefinition of market segments are highly required because defined market segments will change in time. Also as it is difficult to maintain the same level of customer satisfaction with a given set of product mix or marketing strategy, it is essential for companies to match the market offering with the market segments' changing characteristics or expectations (Freytag Clarke, 2001). Similar with other consumer or business markets, market segmentation has been a valuable tool for travel markets as well and it is frequently used in tourism marketing research. According to a wide review of the tourism marketing literature, the majority of existing research is devoted to developing structural frameworks within which a significant portion is spared for identification of homogeneous tourist groups based on segmentation studies (Dolnicar & Ring, 2014). One of the early studies was carried out by Mazanec (1984) that guided on the use of cluster analysis for segmenting tourism markets and since then market segmentation has been one of the most frequently explored areas in tourism research. Different variables are selected to identify different tourist segments in markets such as price sensitivity (Masiero & Nicolau, 2012), expenditure patterns (Lew & Ng, 2012), travel expenditures (Mok & Iverson, 2000), activities (Mumuni & Mansour, 2014) or motivations (Gnoth, 1997). If the tourism product is conceptualized as a “satisfying experience” where the travels of tourists “may be differentiated by the experience sought (product) and the discrete services necessary for its attainment (plant)” (Taylor, 1980: 57), the market offering of the tourism product may be evaluated as a combination of the destination attributes (plant) and motivations or expectations of tourists from that destination (product). Motivations or expectations refer to behavioral variables that are underlined in market segmentation studies (Gnoth, 1997) and they are generally analyzed with benefit segmentation approaches in tourism markets that are curious about identifying the underlying motivations in tourism consumption patterns. Benefit segmentation was initially used by Haley (1968) who defended that traditional descriptive variables used in market segmentation were incapable of identifying market segments effectively and benefit segmentation was more powerful due to its causal structure which explained the benefits that people seek in consuming a given product. This method has gained a significant attention from tourism research in segmenting markets (Sarigollu & Huang, 2005). It is applied to four different areas in tourism management (Frochot & Morrison, 2000) such as (1) destination marketing (e.g.; Andreu et al., 2005; Prayag et al., 2015; Rudez, Sedmak & Bojnec, 2013), (2) targeting specific markets (e.g. Calantone & Johar, 1984; Woodside & Jacobs, 1985), (3) attractions, events and facilities (e.g. Ahmed, Barber & Astous, 1998; Chiang, Wang, Lee, & Chen, 2015; Hsieh, O’Leary, Morrison, & Chiang, 1997), and (4) examining traveler decision making processes (e.g. Gitelson & Kerstetter, 1990; Schul & Crompton, 1983). Pushepull framework is one of the frequently used frameworks to understand tourist motivations in their travel decisions (Crompton, 1979). Its wide acceptance is due to its ability to explain

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why tourists choose a destination instead of another one, the type of experiences and activities they wish to find at that destination (Chen, Mak & McKercher, 2011). While push factors are mostly related with the needs or desires of travelers for travel (Yang, Reeh & Kreisel, 2011), pull factors are a destination's attributes (Mill & Morrison, 1998) or perceptions and image about a destination. For determining successful destination marketing strategies, it is important to understand the interactions between pull and push factors related with that specific destination (Pyo, 2015). 2.2. Benefit segmentation in destination marketing Destinations are continents, countries, states, cities, villages and special resort areas that attract visitors for their travel and hospitality needs (Pike, 2004). Successful destination marketing includes four main activities (Kotler, Bowen & Makens, 1999): (1) designing the right tangible and intangible mix, (2) setting an attractive price policy in exchange for the benefits that users get from the destination, (3) delivering the destination's goods and services in an effective way and (4) promoting the destination in order to inform and encourage existing and potential users. Following this conceptualization, destination marketing is a complex term that includes all the services and experiences that are provided to consumers by a specific destination (Buhalis, 2000). In order to design the right tangible and intangible mix that a destination provides, firstly the needs and expectations of the target markets should be analyzed. This requires an effective segmenting, targeting, positioning strategy and benefit segmentation is among the frequently used methods in travel and hospitality studies. Similar with its generic definition, benefit segmentation in destination marketing studies provides causal clues in explaining the benefits that travelers seek in selecting a specific destination. As suggested by Goeldner and Ritchie (2003) and Kotler et al. (1999) behavioral approaches to segmentation such as benefits and motivations are the most effective predictors of tourist decisions. Benefit segmentation has been used in many destination marketing studies with reference to motivational theories. Frochot (2005) used this method in order to classify the profiles of rural tourists in Scotland. A similar segmentation study was held in South Korea again for rural tourism by Park and Yoon (2009). Prayag et al. (2015) analyzed Western Europe as a destination for Chinese travelers and adopted a benefit segmentation methodology again. Mohsin (2005) tried to explore and group the motivations and attitudes of West Malaysian tourists regarding North Australian holiday destinations. Andreu et al. (2005) segmented British tourists visiting selected holiday resorts in Turkey with reference to their motivations and socio-demographic characteristics. One recent study focuses on tourists from United Arab Emirates and tries to segment this market with reference to their motivations in visiting luxurious destinations such as Paris (Prayag & Hosany, 2014). Although there are many different motivation theories and models used in tourism research the most frequently used one is the pushepull framework (Chen et al., 2011; Dann, 1977). “Pull factors include tangible resources such as beaches, recreation facilities and historic resources as well as travelers' perception and expectation such as novelty, benefit expectation and marketed image of the destination” (Baloglu & Uysal, 1996: 32). Safety and security has been among the most highly evaluated pull factors in the literature. Followers are accessibility, culture and historical resources, weather, natural beauty, variety of shopping facilities and restaurants, special events, night life, cultural events, accommodation options, attitude of local community towards tourists lu & Uysal, 1996; and price policy (Araslı & Baradarani, 2014; Balog Correia, Valle, & Moço, 2007; Cracolici & Nijkamp 2008; Hsu, Tsai &

Wu, 2009; Sukiman, Omar, Muhibudin, Yussof, & Mohamed, 2013; Zainuddin, Radzi & Zahari, 2013). “Most of the push factors which are origin related are intangible or intrinsic desires of the individual travelers such as the desire for escape, rest and relaxation, health and fitness, adventure, prestige, lu & Uysal, 1996: 32). Escape from and social interaction” (Balog routine, rest and relaxation, adventure seeking, meeting different cultures and life styles, spending time with family and friends and meeting new people are among the most frequently used ones in the literature (Beerli & Martin, 2004; Correia et al., 2007; Jang & Wu, 2006; Hanoquin & Lam, 1999; Hsu et al., 2009; Hung & Petrick, 2011; Kim, Lee & Klenosky, 2003). Pull factors are used in order to explain the destination choice by referring to what the destination offers to attract travelers; push factors are used to understand the reasons behind internal desire and motivation to travel (Pyo, 2015). These two sets of factors are interdependent and related (Kim, Noh & Jogaratnam, 2006) and therefore their interaction is also important (Baloglu & Uysal, 1996). An ideal service and experience package for a traveler is the one that is perceived favorably on specific product attributes composed of pull and push factors that are important for that specific traveler (Pike & Page, 2014). Therefore, to satisfy a target segment, it is essential to understand what the segment perceives as important and to provide the market offering favorably on those specific attributes. 2.3. Marketing marinas as destinations: The Turkish case Marine tourism in general refers to all activities that are hosted by or are focused on the marine environment and involve the travel away from one's place of residence (Orams, 1999: 9). Considering the wide scope of this definition, marine tourism includes a very diverse set of activities ranging from boat chartering, cruising, sea sports to all beach activities, marine wildlife observations or visits to fishing villages (Lück, 2007). This diversity also prevails for the characteristics of marine tourists that prefer different marine tourism activities. Marine tourism is growing at a higher pace when compared with the general tourism industry. There are two main reasons for this trend. Firstly, specific marine recreational activities such as surfing, whale watching, snorkeling have become popular themselves. The second reason is that specific locations have-become popular destinations for marine tourism activities (Orams, 1999). Marinas can be considered among these popular destinations and they are a sub-industry of nautical tourism. Nautical tourism is defined as the navigation and stay of nautical tourists on their vessels and in nautical ports for the purpose of relaxation and recreation (Bartoluci & Cavlek, 1998 cited by Jovanovic, Dragin, Armenski, Pavic, & Davidovic, 2013: 859). According to Lukovic (2013) nautical tourism is a multifunctional activity that contains a special maritime component and it is composed of three sub-industries which are marina industry, charter industry and cruise industry. Nautical tourism has been subject to scholarly research in terms of country-specific cases. For example, general nautical tourism activities of Serbian travelers were analyzed in order to discover the constraining factors for these activities (Jovanovic et al., 2013). Pranic, Marusic and Sever (2013) analyzed cruise passengers' experiences regarding the coastal destinations that cruises call in Croatia. Wood (2004) emphasizes that cruises have become the destinations themselves regardless of their rotations due to the developments in ship building industry and cruise voyage marketing strategies. Jugovic et al., (2011) propose an alternative and systematic model for the sustainable development of nautical tourism ports in Croatia. Another Croatian study proposes a better utilization of coastal

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space through location selection for marinas to be established in Split and Rijeka (Favro & Kovacic, 2015). The conflict between marine sports and marinas is explored with a special focus on marinas located at Tenerife (Gonzales, Marrero & Santana, 2010). Despite this body of scholarly research on nautical tourism, a destination perspective towards marinas seem to receive nascent attention. Therefore, it may provide useful insight to define marinas which are components of nautical tourism system as touristic destinations. International Marina Institute (IMI) defines a destination resort marina as a place “accessible by land and by water, including berth places for visitors, accommodation, dining facilities, swimming pool and other entertainment and leisure facilities that provide a resort atmosphere” (IMI, 1998). Heron and Juju (2012) mention that marinas are much more than just mooring facilities by emphasizing that today's marinas should provide a wide range of facilities. Furthermore, they also state that marinas should be defined as holistic hospitality businesses rather than just stand-alone operations. Stone (2000) shares the same approach and defines today's marinas as tourist destinations. He states that since people combine their love of boating with that of travelling; recreational boating facilities have emerged. Arlı (2012) takes a different perspective and divides marina services into two main functions. The first one is the people-oriented function that includes the services designed and developed for yacht owners and their captains. The second one is the vehicle-oriented function that focuses on yachts or boats served by the marina. He describes marinas as tourism companies which provide accommodation to private and commercial motor yachts or sail yachts and their owners and captains (Arlı, 2012). If a destination is described as a “diverse and eclectic range of businesses and people, who might have a vested interest in the prosperity of their destination community (Pike & Page, 2014: 203)” then marinas as yacht harbors composed of many different businesses that supplement each other for providing the ultimate market offering to both people and vehicles that choose their resort can be called destinations as well. Also with reference to IMI (1998) definition, a resort marina is conceptualized as a destination with many different facilities being served. Once marinas are defined as destinations, destination marketing principles can be applied to these facilities in order to develop marketing strategies, position or reposition their market offering and analyze their customer base. However, the literature on destination marketing seems to neglect marinas as units of analysis. Similar with other leisure travel destinations, marinas need to group their customers into meaningful segments in order to match their market offering with the characteristics of specific market segments (Hanlan et al., 2006). Because of their dual structure e sea interface and land interface e and because of the variety of services they provide, targeted differentiation strategies based on customer preferences are essential for successful marina management (Nas & Cos¸ar, 2014). Similarly, Heron and Juju (2012) stresses that marinas should create their “unique selling proposition” to distinguish themselves from the rest. The uniqueness elements can be dry berth facilities, boat repair yards, the number of berths, broad range of facilities or customized pricing structures. This can be achieved through a deep market analysis followed with market segmentation, targeting selected segments and positioning the market offering according to customer needs (Heron & Juju, 2012). Mediterranean basin is a highly preferred location by yachters. Countries like France, Spain, Italy have benefited this market with their marinas as first movers and wise investors. East Mediterranean countries like Greece, Croatia, and Tunis have become important competitors to West-Med with their relative price advantages. It is estimated that there are more than 600.000 yachts travelling around in Mediterranean area (Eris¸, 2007). Out of 19.000

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marinas and yacht harbors available in the world, 5000 are located on European coasts (TCS, 2014). Although Turkey has an important competitive position with regard to its suitable geography, appropriate climate, and service quality (Eris¸, 2007), it is behind its competitors in terms of marina investments with respect to its coastal length. Italy has 380 marinas on its 6500 km coastline, Turkey has 46 marinas on its 8333 km coastline (TCS, 2014). In Turkey, yacht harbors are classified according to their special characteristics and are labelled similarly with hotels but with anchors instead of stars (Official Gazette, 2009). Turkey Marine Tourism Legislations describe these labels as 3, 4 or 5 anchors. On the other hand, marinas in Turkey are classified as main yacht harbors, secondary yacht harbors, yacht berthing areas and dry docks according to 08.06.1983 dated and 83/6708 numbered acts of Board of Ministers (Güner, 2004). First yachts were seen in 1965 on the Aegean coast line but they were carrying tourists that were visiting Greece (TCS, 2014). Turkish marina industry was initiated in 1970 by Turban Tourism A.S¸. which was a government investment. First marinas were located in Kus¸adası, Bodrum and Kemer and in 1974, Çes¸me Altınyunus Marina was established as the first private marina initiative (Eris¸, 2007). After 1980, marina needs and capacity of Turkey's coast line were evaluated by a master plan to undertake significant improvements in the yachting industry similar with the overall tourism industry. Generally, while BodrumeFinike is a primary and most intensive route, BodrumeKus¸adası and FinikeeAntalya are among favorable rotations for yachters (http://www.kugm.gov.tr). The number of marinas in Turkey reached to 46 as of 2014 with an increase of 94% when compared with 25 marinas at the end of 2002. Additionally, there are 69 mooring facilities and total mooring capacity is 25.199 including yacht harbors, municipality harbors, dry docks and others. As it is the case with world marinas, there are very few studies on Turkish marinas. Existing studies take a managerial perspective and reflect the managers' perceptions on marina services or marina marketing strategies. To the best of the authors' knowledge, there are no market segmentation efforts applied on marina customers yet. This study tries to take a destination marketing perspective to Turkish marinas located on the Aegean coast and uses a factorcluster methodology in order to determine the existing marketing segments in this market. The study adopts a benefit segmentation approach and uses the pushepull framework. In addition, potential differences between market segments depending on specific socioedemographic characteristics are explored. 3. Research design 3.1. Questionnaire development This study adopted a mixed methodology approach consisting of qualitative and quantitative stages to provide greater empirical support to the topic in question. Firstly, in order to obtain the items to be used in the questionnaire extensive reviews of destination marketing and pushepull framework literature were conducted. Throughout the qualitative stage of this study, secondary data were collected in conferences and seminars on marine tourism and marinas held in 2014, through unstructured interviews with 3 marina managers, 1 marina supplier and structured interviews with 2 marina managers and 1 academician working on marina marketing. As a result of these data collection processes, 50 marina features and 35 individual motivation items were listed as inputs for questionnaire items and they were presented with their observation frequencies to 8 scholars studying on marine tourism. The scholars recommended elimination or combination of several items. The refined version of the questionnaire was pre-tested on 10 yacht

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owners in order to assure that the items were directly related with yacht customers and they were perceived correctly with appropriate terminology. At the end of this process, 38 marina selection variables or pull factors (34 of them previously used in destination marketing literature, 4 of them added by the recommendations of industry representatives), and 12 individual motivation variables or push factors which all have been used in the literature frequently (except one of variables added by the interviewees) were selected for the final questionnaire (Appendix 1&2). The questionnaire was composed of 4 main parts. First part included various socioedemographic variables such as gender, age and income level, questions regarding yachting characteristics such as yachting experience, yacht length, information sources used for marina selection, marina deciders and importance of destination surrounding the marina. These were used both for the general description of the sample and also for the validation of the differences between obtained clusters at the end of the analysis. The second part contained the expectations from marinas (pull factors) and the third part contained the motivations for traveling to marinas (push factors). The items at these sections were evaluated on a 5-point Likert scale with reference to their perceived importance (“1” not important at all, “5” very important). A final part was added to receive precious clues in the form of free recommendations from respondents for a better interpretation of the research results.

3.2. Sampling process As the objectives of this study are to provide an exploratory effort for evaluating marinas as destinations and to identify the customer segments among the yachters traveling to marinas, the scope of the research was delimited with a specific region. Aegean coast of Turkey was selected as the target region, yachters visiting this region were selected as the source market and seven marinas located on this coast were determined as the destinations. The questionnaire form was applied to marine-side customers of Levent Marina, Port Alaçatı, Setur Altınyunus, IC Çes¸me, Teos Marina, _ Didim D Marin and Setur Kus¸adası marinas located in Izmir and Aydın provinces on the Aegean coast of Turkey. Mooring side of marinas is the best place to reach yachters, as they tend to spend most of their time at seaside when they berth in marinas. A non-random sampling method was preferred to reach as many respondents as possible. The researchers determined the target sample as yacht owners or renters since they were assumed to be the decision makers of the marina selection decision. The respondents that have specific expertise or experience related with the research topic were chosen purposively in order to reflect the population as exactly as possible, therefore judgmental sampling method was selected (Cooper Schindler, 1998). All available yachters at the selected marinas during the data collection process were targeted as the sample. In order to eliminate language barriers

Table 1 Exploratory factor analysis for marina selection items. Factors:

Mean (SD)

F1

F2

F3

F4

F5

F6

F7

F1: Service Attitudes of staff toward marina customers Cleanliness and hygiene conditions Security Service quality Infrastructure quality Safety Environmental friendliness Price policy Having a well-equipped dry dock Weather Factor 2: Prestige Being luxurious Image of the marina and being well-known 5 golden anchors Factor 3: Accessibilities Located nearby the city center Easy access to the destination Easy access to the marina Closeness to airport Factor 4: Touristic Attractiveness Closeness to other attractive destinations Closeness to transit or trip routes Cultural and historical resources in the destination involved Factor 5: Local Culture Unique and interesting culture of local people Similar culture of local residence Attitudes of local community toward tourist Factor 6: Entertainment Event Individual activity opportunities Beaches for swimming and sun tanning Factor 7: Supportive Elements Accommodation facilities Information and tourist service Having appropriate social environment for yacht crews Eigenvalues Explained variance by factors (%) Cronbach's alpha

4.64 4.66 4.77 4.69 4.68 4.61 4.37 4.41 4.34 4.16

(.72) (.78) (.60) (.78) (.76) (.73) (.93) (.91) (1.05) (.99)

.86 .85 .85 .80 .80 .76 .69 .67 .62 .49

2.89 (1.28) 2.74 (1.35) 3.18 (1.39) 3.57 3.79 4.16 2.68

.81 .76 .71

(1.35) (1.22) (1.04) (1.45)

.77 .71 .67 .57

3.57 (1.17) 3.70 (1.22) 3.14 (1.22)

.83 .80 .59

2.70 (1.24) 2.53 (1.26) 3.43 (1.36)

.87 .76 .50

2.82 (1.32) 2.54 (1.22) 3.39 (1.37)

.71 .69 .43

2.49 (1.31) 3.32 (1.12) 2.79 (1.38)

61.3 .83

Measured on a 5-point scale: [1] not at all important; [5] very important.

.77 .61 .58 6.07 20.92 .91

2.24 7.73 .73

2.08 7.18 .68

2.02 6.96 .68

2.01 6.94 .66

1.69 5.82 .53

1.68 5.80 .55

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all respondents were selected among customers who can understand and speak either English or Turkish. A total of 261 responses were collected between August and September 2014. This sample size is similar with previous segmentation studies applied in destination marketing literature (e.g. Arimond & Elfessi, 2001; Park € & Yoon, 2009; Ozel & Kozak, 2012; Agapito, Valle & Mendes, 2014; Prayag & Hosany, 2014).

3.3. Profile of the sample The sample is composed of yachters that are mostly welleducated men (73%), aged above 40 (64%), married or having a long-term relationship (78%) and earning high income levels (62%). Most of them are Turkish (86%) who are living in Izmir (52%) and managing their own companies (38%) or working for private companies (37%). Their yachting experiences are longer than 4 years (86%) and their yachts' are less than 12 m (79%). It was observed that they do not prefer paid crew (61%) and their tendency is to make a contract with marinas for 12 months (70%) which is the longest agreement length. Summer is their favorite season (86%), while winter (15%) is the off season. In addition to these, yachters are taking marina selection decisions by themselves or at least as a family (85%) and select the marina they will call depending on its destination (76%). Friends' and relatives' recommendations are primary sources of marina information (64%). Part of them are taking the marina selection decision after visiting marinas in person (35%) and especially foreigner yachters prefer to follow tourism agencies' recommendations (13%).

3.4. Data analysis Cluster analysis was used to segment the sample into homogeneous customer groups. This method is an analytical technique and it has been widely used by marketing studies aiming to segment target markets. Although the most common cluster techniques are hierarchical and nonhierarchical K-means, they both have some weaknesses in terms of cluster set selection and outlier (noise) detection (Kumar, Tan & Steinbach, 2014). Many researchers suggest a combination method that firstly uses a hierarchical technique to decide an appropriate number of clusters by inspecting coefficients in the agglomeration table and then employs a non-hierarchical method for both elimination of outliers and clustering the remaining observations to cope with these problems. Comparing obtained clusters with Anova or crossclassification table (using independent variables such as age, gender which are not used to obtain the clusters) or comparing two different cluster solutions with each other by randomly splitting the sample in terms of number of clusters and the cluster profiles (Hair, Black, Babin, & Anderson, 2010: 450) have been advised as cluster validation techniques. Additionally, independency of variables is an important issue for the efficiency of obtained clusters. Therefore, before conducting a cluster analysis, application of factor analysis guarantees that variables are not strongly correlated with each other (Hair et al., 2010). In the literature, factor-cluster analysis methodology is adopted by many studies (Arimond & Elfessi, 2001; Chung, Oh, Kim, & Han, 2004; Jun & McCleary, 1999; Liu, McCarthy & Chen, 2013; Mumuni & Mansour, 2014; Park & Yoon, 2009; Rudez et al., 2013; Schlager & Maas, 2013). It is also recommended because the combination of these two methodologies helps to reduce large number of benefit statements into a manageable set of factors (Frochot & Morrison, 2000).

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4. Findings 4.1. Segment identification Following the factor-cluster approach to market segmentation, firstly exploratory factor analysis (EFA) was conducted to identify the dimensions under 38 marina selection items. Varimax method was used to rotate the factors and factors with eigenvalues greater than 1 were retained. 9 variables were eliminated since they were either double loaded onto different factors or had less than .4 factor loadings. KaisereMeyereOlkin Measure of Sampling Adequacy was calculated as .84 and Bartlett's Test of Sphericity was significant at .000 level. The overall Cronbach's alpha was calculated as .83. At the end expectations from marinas or the pull factors were reduced to 7 dimensions that explained 61.3% of the total variance. Based on the patterns of factor loadings, that are given on Table 1, dimensions were labelled as service, prestige, accessibility, touristic attractiveness, local culture, entertainment and supportive elements. Afterwards, cluster analysis was conducted in two stages. Firstly, hierarchical cluster analysis was employed by using marina expectations (pull factors) as inputs. Ward linkage method and Euclidian distance measures were selected. Thereafter, agglomeration schedule was examined and approximately a five cluster solution was found reasonable. Then, K-means clustering was run for a 5 cluster solution, and the final clusters were obtained. Throughout this process, seven responses were detected as outliers (noise) and 14 responses were excluded because of missing data. Thus, the final sample size was accepted as 240. One-way ANOVA results were examined and it was observed that clusters' means were significantly different from each other. As seen on Table 2, each cluster was labelled based on the relatively highest or lowest mean centered factors: socially oriented, indifferent, supportive facilities oriented, service and prestige oriented and touristic attractiveness oriented clusters. According to the results, touristic attractiveness oriented cluster is the largest segment among others with its 38% share. Its touristic attractiveness factor's mean is sharply different than others. On the other hand, socially oriented, supportive facilities oriented, and service and prestige oriented clusters' sizes are approximately the same (18%) and they are differentiated from each other according to the highest factors' means. While socially oriented segment's entertainment factor score is higher than others, supportive elements have a relatively higher importance for supportive facilities oriented customer segment and service and prestige factors are more important for service and prestige oriented segment. Indifferent cluster's difference was the customers' low evaluation for the importance of service attributes. In general, service factor was calculated to be the most important factor for yachters and its combined mean was very high (4.53); interestingly, indifferent cluster's mean for this factor is quite low (1.95). On the other hand, this cluster's share in the overall market is also relatively low (9%) and the customers in this segment seem to have different characteristics and motivations for traveling to marinas. In addition to these, socially oriented cluster's local culture factor's mean is higher and touristic attractiveness factor mean is lower than other clusters. Supportive facilities oriented cluster has the lowest mean for entertainment factor among all clusters while the importance attained to accessibility factor by this cluster is the highest. 4.2. Validation of the clusters As suggested by Hair et al. (2010), firstly, samples were split up into two parts randomly, and cluster analyses were run for each parts of the sample for assuring validity. The five-cluster solution

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Table 2 Mean centered values of final cluster. Marina selection factors

Cluster1: Socially oriented (N ¼ 44, 18%)

Cluster2: Indifferent (N ¼ 22, 9%)

Cluster3: Supportive facilities oriented (N ¼ 40, 17%)

Service Prestige Accessibility Touristic Attractiveness Local Culture Entertainment Supportive Elements

4.85 2.63 3.49 2.53 3.33 3.66 2.54

1.95 2.89 3.31 3.29 2.71 3.13 3.03

4.81 2.95 4.15 2.91 2.63 1.98 3.72

Cluster4: Service& prestige oriented (N ¼ 43, 18%)

Cluster5: Touristic Attractiveness oriented (N ¼ 91, 38%)

F-ratio

Sig.

4.65 2.77 3.90 4.20 3.16 2.85 2.63

128.85 17.72 27.67 8.02 37.77 27.59 8.61

.00 .00 .00 .00 .00 .00 .00

Mean

was validated by both split-up samples. Thereafter, both push factors (individual motivations for traveling to marinas) and demographic and yachting characteristics of yachters were evaluated as independent variables. They were tested with Anova and ChieSquare tests in order to see if customer segments can be profiled differently or not. The results of these validation steps are given at the discussion part. 4.2.1. Cluster profiling based on individual motivations (push factors) Another EFA was applied to identify the underlying factors examining the 12 individual motivations for traveling to marinas. Varimax method was used to rotate factors and factors with eigenvalues greater than 1 were retained. 2 items were eliminated since they had less than .4 factor loadings. KaisereMeyereOlkin Measure of Sampling Adequacy was calculated as .82 and Bartlett's Test of Sphericity was significant at .000 level. Sample's general Cronbach's alpha was calculated as .84. The overall push factors were loaded onto 3 main dimensions which explained 61.3% of the total variance. Based on the patterns of factor loadings, as seen on Table 3, factors were labelled as social, adventure and freedom. Afterwards, one-way ANOVA test was applied to obtained clusters by setting cluster membership as the dependent variable and the push factors as the independent variables. Since the results of Anova indicated statistically significant differences among groups, Tukey-HSD test was run in order to detect the factors that were significantly different from each other. As a result of this analysis, freedom dimension was observed to have a significant difference among customer groups (Table 4). Cluster 2 (indifferents) attaches the lowest perceived importance to this

Table 3 Exploratory factor analysis for individual motivations (push factors). Factors:

Mean (SD)

F1

Meeting people with similar interest Developing close friendship Self-esteem and social recognition Meet different cultures and life style Factor 2: Adventure

3.79 3.55 3.07 3.73

0.84 0.79 0.75 0.49

Adventure seeking Novelty seeking Having fun Factor 3: Freedom

3.48 (1.29) 3.59 (1.22) 3.96 (1.17)

Being free to act as one feels Escape from routine Health and fitness

4.41 (.91) 4.13 (1.07) 3.95 (1.09)

Eigenvalues Explained variance by factors (%) Cronbach's alpha

64.7 .84

F2

F3

Factor 1: Social (1.14) (1.22) (1.38) (1.15)

0.84 0.72 0.63 0.84 0.81 0.57 2.41 24.11 .79

2.05 20.51 .72

2.00 20.05 .72

5.00 3.62 2.43 3.51 2.19 3.08 2.84

dimension when compared with other segments. 4.2.2. Cluster profiling based on independent demographic and yachting characteristics Hair et al. (2010) suggest that in order to confirm predictive validity of cluster analysis, the researcher might select variable(s) not used to form the clusters but known to vary across the clusters. Throughout the qualitative data collection process in this study, interviewees had given certain clues about potential socioedemographic variables that are expected to vary among clusters such as income level, yacht length, type or crew preference. These socioedemographic variables were used in order to confirm that there were significant differences among the discovered clusters with reference to external variables. ChieSquare Tests were applied to obtained clusters to profile them. As a result of this analysis, gender, marital status, income level, yacht owning, yacht type, number of paid crew on the yacht, yacht length and contract time with homeport exhibited some significant differences between customer segments. The results are listed on Table 5. 5. Discussion and implications The main objective of this study was to consider marinas as touristic destinations and exhibit an exploratory effort to identify the existing segments of yachters using the pushepull framework. Previous studies that have taken managers' perspective to identify the important pull factors for marina success claim that value for money, customer satisfaction and marina services for yachts or customers are among the most important ones (Raviv et al., 2009). However, these studies don't evaluate marinas as destinations; they rather consider them as individual service providers. It is only a very recent phenomenon to consider marinas as touristic attractions that are in relation with the destinations that they are located at (Favro & Kovacic, 2015). This study takes a step further and analyzes marinas as destinations themselves. The study results indicated that yachter customers of marinas do not exhibit unified market characteristics; indeed, they consist of different market segments that are labelled as the socially oriented, indifferent, supportive facilities oriented, service and prestige oriented and touristic attractiveness oriented customer groups. The members of each segment represent different demographic characteristics and expectations in terms of push factors. These differences can be used in order to develop operational marketing recommendations for marina managers (Dolnicar & Ring, 2014). In order to provide useful managerial implications, firstly the overall mean values for pull and push factors were examined. In general marina services for both yachts and yachters seem to have the highest importance levels for the whole sample where security item is considered to be the most important service component. These are followed by accessibility and touristic attractiveness

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Table 4 Cluster profiling based on individual motivations to travel to marinas (push factors). Cluster3: Cluster2: Individual Cluster1: Supportive Indifferent Motivation Socially (N ¼ 22, 9%) facilities oriented oriented factors (N ¼ 40, 17%) (N ¼ 44, 18%)

Cluster4: Service& prestige oriented (N ¼ 43, 18%)

Cluster5: Touristic Sig. Post-hoc results of Tukey-HSD test Attractiveness oriented (N ¼ 91, 38%)

Freedom

4.23

4.15

4.31

3.09

4.36

.00 2&1 (p < 0.01); 2&3 (p < 0.01); 2&4 (p < 0.01); 2&5 (p < 0.01)

Table 5 Cluster profiling based on independent demographic and yachting characteristics. Socio-demographic characteristics

Sample I: Socially oriented II: Indifferent III: Supportive IV: Service& V: Touristic Pearson ChieSquare Sig. size (N ¼ 44, 18%) (N ¼ 22, 9%) facilities oriented prestige oriented Attractiveness (N ¼ 40, 17%) (N ¼ 43, 18%) oriented (N ¼ 91, 38%) Distribution of demographic characteristics of yachters in the clusters

Gender

Male Female Total Marital Status Married-living together Single Total Income Level Mid-Level-less High-over Total Yacht Owning Yacht Owner Yacht Renter Total Yacht Type Sailing Motor Yacht and other Total Paid Crew Number 0 1-over Total Yacht Length <¼12 m >12 m Total Home port Contract time <10 mount 10 mount-over Total

177 63 240 186

26 (59.1) 18 (40.9) 44 31 (70.5)

15 (68.2) 7 (31.8) 22 13 (59.1)

35 (87.5) 5 (12.5) 40 36 (90.0)

30 (69.8) 13 (30.2) 43 33 (76.7)

71 (78.0) 20 (22.0) 91 73 (80.2)

54 240 86 151 237 171 67 238 149 72

13 (29.5) 44 21 (48.9) 22 (51.1) 43 35 (79.5) 9 (20.5) 44 35 (83.3) 7 (16.7)

9 (40.9) 22 11 (52.4) 10 (47.7) 21 9 (42.9) 12 (57.1) 21 7 (41.2) 10 (58.8)

4 (10.0) 40 9 (22.5) 31 (77.5) 40 28 (70.0) 12 (30.0) 40 26 (66.7) 13 (33.3)

10 (23.3) 43 11 (25.6) 32 (74.4) 43 34 (79.1) 9 (20.9) 43 25 (62.5) 15 (37.5)

18 91 34 56 90 65 25 90 56 24

221 133 89 222 165 75 240 50 120 170

42 27 (64.3) 15 (35.7) 42 29 (65.9) 15 (34.1) 44 9 (26.5) 25 (73.5) 34

17 8 (44.4) 10 (55.6) 18 11 (50.0) 11 (50.0) 22 7 (63.6) 4 (36.4) 11

39 16 22 38 23 17 40 11 17 28

40 29 (70.7) 12 (29.3) 41 35 (81.4) 8 (18.6) 43 7 (20.0) 28 (80.0) 35

83 53 30 83 67 24 91 16 46 62

dimensions. The least important pull factor seems to be the local culture for the combined sample. When the factor analysis results for push motivations are examined, consistent findings emphasized the inter-relation between pull and push factors. The highest importance level is attached to freedom dimension which states that yachters travel to marinas in order to escape from their routines and to act as they feel like. The lowest importance level is attached to the social dimension which is composed of meeting people with similar interests or developing close friendships. Although these general results might show some meaningful paths to marina managers, what this study especially proposes is to differentiate the market offering depending on the characteristics of different yachter segments that are revealed by the cluster analysis. In order to do this firstly the expectations of each cluster's members are examined. Then the socioedemographic variables that define the members of each cluster are identified. Finally, implications for marina managers who would like to know and target these customer segments are derived from the analysis. The results are summarized on Table 6. The largest market segment is the touristic attractiveness oriented customer group (Table 2). The yachters in this segment are strongly motivated by the proximity of marinas to other touristic attractions, cultural or historical sites, interesting nautical or

(42.1) (57.9) (57.5) (42.5) (39.3) (60.7)

Chi-square ¼ 10.4, p-value ¼ .04

Chi-square ¼ 9.5, p-value ¼ .05

(19.8) (37.8) (62.3)

Chi-square ¼ 10.7, p-value ¼ .03

(72.1) (27.8)

Chi-square ¼ 11.2, p-value ¼ .02

(67.5) (32.5)

Chi-square ¼ 16.2 p-value ¼ .04

(63.9) (36.1)

Chi-square ¼ 10.4 p-value ¼ .05

(73.6) (26.4)

Chi-square ¼ 10.3 p-value ¼ .04

(25.8) (74.2)

Chi-square ¼ 9.5 p-value ¼ .05

landscape routes. In addition to these, they also attach high importance to service attributes designed both for yachts and yachters. Last, but not the least, they prefer a high degree of accessibility of the marinas that they call. Yachters in this segment generally come from upper income levels. They are mostly couples who are either married or living together. They prefer sailing boats that have easily manageable lengths (below 12 m) because they prefer to sail by themselves instead together with crew. They tend to own their own yachts and sign long-term contracts with their preferred home ports. Marina managers who want to target this segment should promote the historical or natural resources that they are closely located to. Daily trips to nearby nautical routes can be a favorable promotional tool. Marina should be marketed as a destination with many different touristic attractions where service attributes are also provided at a high level. Socially oriented, service & prestige oriented and supportive facilities oriented customer segments are more or less similar in size. However, they have different expectations from marinas. For instance, socially oriented customers are highly interested in the local culture and available entertainment elements besides the must-be requirement for high service levels. For service & prestige oriented customers service attributes and reputational elements receive the highest importance while accessibility or local culture

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seem to be irrelevant. Supportive facilities oriented customer groups, on the other hand, attach high importance to accessibility of the marinas and additional services such as tourist guidance or crew support. This finding is in line with the characteristics of this segment as they are the only segment that mostly prefer paid crew when compared with others. When the socioedemographic characteristics of these three segments are examined together with their expectations from marinas, different suggestions can be derived for marina managers that would like to target each of them. In order to attract socially oriented customer groups, special events at the marina location should be organized and promoted for entertainment. Beaches and other relaxation spots should be marketed. Sponsorships to local events such as festivals, concerts, contests or races would motivate these groups. Also providing space and funds for the exhibition of local cultures at the marina site through entertainment events can be among favorable public relations tools. On the other hand, marina managers that want to attract service and prestige oriented customers should measure and improve their service levels on both yacht and yachter sides. They should aim to position their marinas as a high-segment service provider through luxury accreditations, facilities that can accommodate luxurious yachts, high reputation levels through cooperation with other wellknown and highly-perceived brands in related product segments. In order to attract supportive facilities oriented customer groups, instead of focusing on entertainment elements, managers should promote supportive accommodation services like marina hotels, tourist guidance services, transfer services to airports and other destinations. An important point to emphasize is the service base for the crew. The hardest customer segment is the indifferent customer group as it is difficult to predict these customers' motivations. Their most differentiating characteristics is their lower freedom motivation when traveling to marinas. They prefer motor yachts mostly and they sign short term contracts with their home ports. Actually they seem to be yacht renters rather than owners and they are smaller in size when compared with other customer groups. For converting these yachters to repetitive visitors, special events for entertainment can be regularly communicated. Also touristic attractions located nearby can be promoted. The profile information and the individual motivations of these five clusters require different marketing efforts for targeting different segments. Marina resources are not unlimited and marina managers try to allocate these resources as efficiently as possible while targeting their markets and positioning their services. A deep knowledge about the market segments that they serve may lead their way in this effort. Combining this knowledge with the resources they have such as proximity to historical sites, touristic attractions, interesting routes or availability of regular festivals, races, contests will appeal to various customer groups. Marinas that lack such resources may at least try to develop some of them in cooperation with local authorities in order to market their offering as a touristic destination. Destination marketing literature states that tourists from different source markets (e.g. Beerli & Martin, 2004) or even from the same source market (Prayag & Hosany, 2014) may hold different evaluations regarding the same destination. In this study, the destination is not fixed for the respondents; instead, they were requested to evaluate their motivations for traveling to marinas that they frequently visit. So this research contributes to tourism management literature in terms of studies where both the source and destination markets are dynamic. According to Favro and Kovacic (2015) three fundamental branches of marine tourism that can be listed as ports of nautical tourism (marinas), charters and cruises are closely related with the

destinations that they are located at or simply they stop by. This study is one of the first studies that evaluates marinas as destinations that provide a market offering that exceeds the simple storage and accommodation of boats. The approach adopted in this study can be extended to the other branches of marine tourism such as charter boats or cruises. Actually cruises have been considered as the destination itself rather than a transportation vehicle for quite some time (Wood, 2004) and as unique destinations they compete with land resorts and other well-established destinations (Morrison, Yang, O'Leary, & Nadkarni, 1996). Adding marinas to these new destinations might extend the tourism management literature in terms of strategic marketing of tourism product and achieving competitive advantage for new destinations. The study has its limitations though. Firstly, the sample size might be considered as limited. This limitation might provide avenue for future research that might use the findings of this exploratory study and apply the research design to other yachters traveling to marinas both in Turkey and other regions of the world. Second, any destination study is delimited with the destination and the source markets that are being analyzed. Here it is the Aegean coast and the yachters calling the marinas located on this coast. However, benefit segmentation approach accepts that the segments extracted from each study are unique to the product that is analyzed in that specific study (Haley, 1968). Other unique benefits could be revealed during the analysis of any other product (Frochot & Morrison, 2000). The same considerations prevail for this study as well. However, the results were tried to be validated through several methods. As suggested by Frochot and Morrison (2000) face validity is tried to be secured by demonstrating the clusters' practical value for travel and tourism marketing. However, sharing their reluctance regarding researcher subjectivity, this process was not carried on by the researchers solely; instead the results were shared with 43 marina managers in Turkey and with Chamber of Shipping's Marine Tourism Commission. Their feedbacks on the extracted clusters were requested and the majority confirmed the existing division among yachter markets. In addition to that, following previous studies (e.g. Park & Yoon, 2009; Prayag & Hosany, 2014) and methodological approaches (Hair et al., 2010) the clusters were analyzed statistically with reference to push factors and significant differences were found that confirm the heterogeneity between customer groups. Further statistical analyses were conducted to analyze socioedemographic differences between segments which revealed significant results. Within its scope, this study makes an exploratory effort for the discovery of underlying customer segments among the yachter customers of marinas located on Turkey's Aegean coast. This effort can be extended to other regions, other marina types (like urban marinas) or to a single country's overall marinas for a further understanding of underlying customer segments from different perspectives. The same study can be applied to other regions in the world in order to identify differences and conduct comparative analysis among countries or widely acknowledged touristic regions. Promising results can be derived by comparison studies among marinas located on holiday districts and in urban locations as the motivations of yachters to call these destinations are expected to differ significantly. Recently new trends are being observed in the marina industry such as “live aboard” yachters or super and mega yacht owners. These customers have completely different motivations in choosing their home ports. Also the services that they seek differ. Super and mega yachts are observed relatively highly in European and North American marinas (Heron & Juju, 2012). Scholars may analyze those regions to detect any possible customer segments together with their characteristics and expectations.

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Table 6 Cluster implications for practitioners. Clusters

Cluster's expectations from marinas Socioedemographic characteristics Recommendations for practitioners

Touristic attractiveness - Touristic attractiveness oriented cluster - Interesting local culture - Easily accessible - Service factor's quality should be at appropriate level

- Upper middle income level - Married/living together - Yacht length is either equal or less than 12 m.

Socially oriented cluster

- Yacht owners - Sailing preferred - Don't prefer paid-crew

Service & prestige oriented cluster

Supportive facilities oriented cluster

Indifferent cluster

- Entertainment factor elements; events, individual activity opportunities, beaches for swimming and sun tanning - Interesting local culture - High service level expectations - Well-known prestige of a marina - Luxury - Having 5 golden anchors - High service level expectation - Accessibility is less important

-

High income level Yacht owners Don't prefer paid-crew Yacht length is either equal or less than 12 m. - The longest contract time with home port marina

- Accommodation, information and - Male - Married/living together tourist services - High income level and an appropriate social - Preferred paid-crew environment for yacht crew - Easily accessible - Entertainment elements are not important - Short time visitors, - Lowest expectation level - Yacht renters towards service levels - Having tendency to sign the - Entertainment shortest contract time with home port marina - Motor yacht preference  12 m yacht length - Single - Freedom factor is relatively less important

Moreover, the analysis can be extended to other customer groups visiting marinas as destinations. Marinas provide a wide variety of services appealing to land-side customers as well. Some of the respondents in this study underlined that land-based customer expectations would differ when compared with their expectations. Therefore, an analysis of those customer groups would yield in different market segments that marina managers can select to target. Various market offerings can be designed in order to appeal sea-based or land-based customers once the existing segments are known in depth. In addition to these, a study on internal customers that supplement marina services such as yacht renting agencies, sailing schools, water sports service providers, dry dock suppliers, hotels and restaurants can provide alternative perspectives for marina managers. These internal customers contribute to the final market offering and the destination as a tourism product, so their expectations are significant for the final market success of marinas. Closing the gap between these perspectives and customer expectations might provide fruitful results in terms of marina service performance and quality. Last, but not the least, the study should be repeated in time in

- Marina's proximity to other attractive destinations should be emphasized - Being located on a transit route or a longer trip route is an advantage - Having cultural and historical resources at the destination is an advantage, if available should be underlined - Destination publicity should be undertaken by marinas via guide books, brochures or promotional films that inform the yachters about the destination attractions - Cooperation with local tourism agencies would enhance the market reach - Providing tourist guidance services or employee training on local attractions would differentiate the marina - Close relationships with the local society may result in favorable cooperation with local markets and marina services - Any entertainment activity such as concerts, shows, bars, festivals would support the liveliness of the marina - Guided special night tours in the destination can be arranged for these customers

-

Having a well-known trademark Extensive reputation management Monitoring perceived image by customers continuously with surveys Participating in regional boat shows and advertising in yachting publications and newspapers, improving public relationships Keeping the service level as high as possible all the time Exploring customer expectations through extensive customer relationship management Having good accommodation facilities would attract this segment A social environment for yacht crew should be provided Tourist guidance services, maintenance and repair services and all other assistance services are essential

- It is difficult to estimate this segment's expectations. - Social activities might attract them to call marinas for relatively shorter times. - High levels of their satisfaction can result in repetitive visits

order to follow the changes in existing segments depending on economic trends, competition, variances in tastes and preferences (Haley, 1968). Woodside and Jacobs (1985) underline the requirement to repeat benefit segmentation studies within time because the results might not prevail due to the changes in the external or internal environment surrounding the market offering. 6. Conclusion Today's marinas, as mentioned by Heron and Juju (2012), are not just mooring facilities. Some of them have “… accommodation/ dining facilities, swimming pools and other entertainment and leisure facilities” and they “provide a resort atmosphere” (IMI, 1998). So they can be accepted as tourist destinations having customers who love boating and traveling at the same time (Stone, 2000). Yachters have been described as customers with high expectation levels and they don't have the same motivations to travel to a marina. So, it is essential for effective destination marketing to understand both push or pull motivations factors of yachters, and to determine their distinctive needs from each other's. This study contributes to the tourism literature by extending the current

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research on benefit segmentation to marinas as touristic destinations. The study also tries to respond Dolnicar and Ring (2014) call by developing some operational marketing recommendations for marina managers. It is strongly suggested that research on marinas and marina marketing is extended with additional customer segmentation efforts, market profiling and targeting cases, differentiation opportunities and their impact on marina service performance. Critical incident analysis, quality function deployment

Variables 1- Location nearby the city center 2- Closeness to airport 3- Easy access to the destination

4- Easy access to the marina

5- Closeness to one's residence 6- Prompt contact facilities to emergency points (fire station, ambulance etc.) 7- Closeness to transit and/ or trip routes 8- Closeness to the other attractive destinations 9- Cultural and historical resources at the destination 10- Weather conditions (Wind, temperature, wave characteristics etc.) 11- Natural attractiveness

12- Beaches for swimming and sun tanning 13- Being quiet and not overcrowded 14-Variety of shopping facilities

15- Variety of restaurants and cafeterias

16- Events (Race, concert, festival, art exhibitions etc.) 17- Individual activity opportunities (Library, hobby gardens etc.) 18- Night life

19- Having appropriate social environment for yacht crews 20- Sports facilities (fitness center, water sports etc.)

studies, service quality measurement and customer satisfaction analysis are among methods that might be employed in order to understand the customer base of marinas and design, develop or improve the service provision in these organizations. Appendix 1. “Expectations/Pull factors” for Marinas

Quantitative studies

Qualitative studies Sariisik, Turkay and Akova, 2011

Hanoquin & Lam, 1999; Kim, Crompton and Botha, 2000; Chen, & Gürsoy, 2001; Kim et al.,2003; Enright and Newton, 2004; .Zhang, Qu, and Tang, 2004; Correia, Jang and Wu, 2006; Valle, and Moço, 2007; Hsu et al.,2009; Sukiman et al., 2013; Zainuddin, Radzi, and Zahari,2013; Araslı & Baradarani, 2014 Hanoquin & Lam, 1999; Kim, Crompton and Botha, 2000; Chen, & Gürsoy, 2001; Kim et al., 2003; Enright and Newton,2004; Zhang, Qu, and Tang, 2004; Correia, Jang and Wu, 2006; Valle, and Moço, 2007; Hsu et al.,2009; Sukiman et al., 2013; Zainuddin et al.,2013; Araslı & Baradarani, 2014 Huybers, 2004, Zhang, Qu and Tang, 2004; Correia et al., 2007

McCalla, 1998 Fallon and Schofield, 2006; Raviv et al., 2009; Wang et al., 2014

Fallon and Schofield, 2006; Raviv et al., 2009; Wang et al., 2014

Raviv et al., 2009

Recommended by interviewees

Recommended by interviewees McCalla, 1998 lu & Uysal, 1996; Kim et al., 2003; Beerli & Martin, 2004; Balog Huybers, 2004; Zhang, Qu and Tang, 2004; Yooshika and Uysal, 2005; Jang & Wu, 2006; Correia et al., 2007; Cracolici and Nijkamp, 2008; Hsu et al., 2009; Sukiman et al., 2013 lu & Uysal, 1996; Hanoquin & Lam, 1999; Kim, Crompton and Balog Botha, 2000; Beerli & Martin, 2004; Enright and Newton, 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Jang & Wu, 2006; Correia et al., 2007 lu & Uysal, 1996; Kim, Crompton and Botha, 2000; Kim et al., Balog 2003; Beerli & Martin, 2004; Enright and Newton, 2004; Huybers, 2004; Zhang, Qu and Tang, 2004; Correia et al., 2007; Cracolici et al., 2008; Sukiman et al., 2013 lu & Uysal, 1996; Beerli & Martin, 2004; Zhang, Qu and Tang, Balog 2004; Yooshik and Uysal, 2005; Correia et al., 2007; Sukiman et al., 2013 Huybers, 2004; Yooshik and Uysal, 2005

Fallon and Schofield, 2006; Sariisik, Turkay and Akova, 2011; Wang et al., 2014

Hanoquin & Lam, 1999; Beerli & Martin, 2004; Enright and Newton, 2004; Zhang, Qu and Tang,2004; Yooshik and Uysal, 2005; Jang and Wu, 2006; Correia et al., 2007; Cracolici et al., 2008; Hsu,Tsai and Wu, 2009; Sukiman et al., 2013; Araslı & Baradarani, 2014 lu & Uysal, 1996; Kim, Crompton and Botha, 2000; Yooshik Balog and Uysal, 2005; Kim et al., 2003; Enright and Newton, 2004; Beerli & Martin, 2004; Zhang, Qu and Tang, 2004; Correia et al., 2007; Hsu,Tsai and Wu, 2009; Sukiman et al., 2013; Araslı & Baradarani, 2014 lu & Uysal, 1996; Kim and Crompton and Botha, 2000; Enright Balog and Newton, 2004; Huybers, 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Jang & Wu, 2006; Sukiman et al.,2013; Araslı & Baradarani, 2014 Huybers, 2004

McCalla, 1998; Fallon and Schofield, 2006;

Fallon and Schofield, 2006; Wang et al., 2014

Raviv et al., 2009; Sariisik, Turkay and Akova, 2011; Wang et al.,2014

McCalla, 1998; Sariisik, Turkay and Akova, 2011

Raviv et al., 2009

McCalla, 1998; Fallon and Schofield, 2006

Sariisik, Turkay and Akova, 2011; Wang et al., 2014

lu & Uysal, 1996; Beerli & Martin, 2004; Enright and Newton, Balog 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Correia et al., 2007; Sukiman et al., 2013 Recommended by interviewees

Fallon and Schofield, 2006

Beerli & Martin, 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Jang & Wu, 2006; Correia et al., 2007

Fallon and Schofield, 2006

N. Paker, C.A. Vural / Tourism Management 56 (2016) 156e171

167

(continued ) Variables

Quantitative studies

Qualitative studies

21- Accommodation facilities (Hotel, pension etc.)

Kim, Crompton and Botha, 2000; Lee and Klenosky, 2003; Enright and Newton, 2004; Zhang, Qu and Tang, 2004; Correia et al., 2007; Cracolici et al., 2008; Hsu et al., 2009; Kim, Sukiman et al., 2013; Araslı & Baradarani, 2014 lu & Uysal, 1996; Beerli & Martin, 2004; Yooshik and Uysal, Balog 2005; Jang & Wu, 2006; Sukiman et al., 2013; Araslı & Baradarani, 2014

McCalla, 1998; Fallon and Schofield, 2006; Wang et al., 2014;

22- Cleanliness and hygiene conditions 23- Having blue flag 24- Service quality (Berthing, mooring, front office etc.) 25- Infrastructure quality (Fuel, water, electricity, toilet, laundry, internet etc.) 26- Having a well-equipped dry dock 27- Staff attitude towards marina customers 28- Local community attitudes towards tourists 29- Safety (Fire, sea traffic etc.)

30- Security (Against external threats)

31- Information and tourist services 32- Environmental friendliness 33- Image and popularity of the marina 34- 5 golden anchors 35- Unique and interesting culture of local people 36- Similar culture of local residence 37- Price policy

38- Being luxurious

Fallon and Schofield, 2006

Beerli & Martin, 2004; Huybers, 2004; Zhang, Qu and Tang,2004; Sukiman et al.,2013; Zainuddin et al., 2013

Raviv et al., 2009; Sariisik, Turkay and Akova, 2011 McCalla, 1998; Raviv et al., 2009; Sariisik, Turkay and Akova, 2011; Wang et al., 2014

Beerli & Martin, 2004; Huybers, 2004; Zhang, Qu and Tang, 2004; Sukiman et al.,2013; Zainuddin et al., 2013

McCalla, 1998; Raviv et al., 2009; Sariisik, Turkay and Akova, 2011; Wang et al., 2014

McCalla, 1998; Sariisik, Turkay and Akova, 2011; Wang et al., 2014 Hanoquin & Lam, 1999; Kim, Crompton and Botha, 2000; Zainuddin et al., 2013 lu & Uysal, 1996; Hanoquin & Lam, 1999; Beerli & Martin, Balog 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Correia et al., 2007; Cracolici et al., 2008; Hsu et al., 2009; Sukiman et al., 2013; Zainuddin,Radzi and Zahari, 2013; Araslı & Baradarani, 2014 lu & Uysal, 1996; Kim, Crompton and Botha, 2000; Chen & Balog Gürsoy, 2001; Beerli & Martin, 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Jang & Wu, 2006; Correia,Valle and Moço, 2007; Cracolici et al., 2008; Hsu,Tsai and Wu, 2009; Sukiman et al., 2013; Zainuddin et al., 2013; Araslı & Baradarani, 2014 lu & Uysal, 1996; Kim, Crompton and Botha, 2000; Chen & Balog Gürsoy, 2001; Beerli & Martin, 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Jang & Wu, 2006; Correia,Valle and Moço, 2007; Cracolici et al., 2008; Hsu,Tsai and Wu, 2009; Sukiman et al., 2013; Zainuddin et al., 2013; Araslı & Baradarani, 2014 lu & Uysal, 1996; Hanoquin & Lam, 1999; Kim et al., 2003; Balog Cracolici et al., 2008; Sukiman et al., 2013

Sariisik, Turkay and Akova, 2011 McCalla,1998; Fallon and Schofield, 2006; Raviv et al., 2009

Fallon and Schofield, 2006; Raviv et al., 2009; Sariisik, Turkay and Akova, 2011; Wang et al., 2014

Fallon and Schofield, 2006; Raviv et al., 2009; Sariisik, Turkay and Akova, 2011; Wang et al., 2014

Raviv et al., 2009; Sariisik, Turkay and Akova, 2011 Beerli & Martin, 2004; Enright and Newton, 2004; Beerli & Martin, 2004; Hsu,Tsai, and Wu, 2009; Zainuddin et al., 2013 Sariisik, Turkay and Akova, 2011 Chen & Gürsoy, 2001; Beerli & Martin, 2004; Enright and Newton, 2004; Yooshik and Uysal, 2005; Correia et al., 2007 Recommended by interviewees Huybers, 2004; Zhang, Qu and Tang, 2004; Yooshik and Uysal, 2005; Jang & Wu, 2006; Cracolici et al., 2008; Hsu,Tsai,and Wu, 2009; Sukiman et al., 2013; Araslı & Baradarani, 2014 Beerli & Martin, 2004; Yooshik and Uysal, 2005

Fallon and Schofield, 2006; Sariisik, Turkay and Akova, 2011; Wang et al., 2014

Appendix 2. Individual motivations to travel to Marinas/push factors

Variables

Quantitative studies

1- Escape from routine

Kim et al., 2003; Beerli & Martin, 2004; Yooshik and Uysal, 2005; Guzman et al., 2006; Correia et al., 2007; Hsu,Tsai,and Wu, 2009; Hung & Petrick, 2011 Yooshik and Uysal, 2005; Jang & Wu, 2006; Hung & Petrick, 2011 Kim et al., 2003;; Yooshik and Uysal, 2005; Hsu,Tsai,and Wu, 2009 Guzman et al., 2006; Hsu,Tsai,and Wu, 2009 Kim et al., 2003; Beerli & Martin, 2004; Yooshik and Uysal, 2005; Correia et al., 2007; Guzman et al., 2006; Jang & Wu, 2006; Hsu,Tsai,and Wu, 2009; Hung & Petrick, 2011 Beerli & Martin, 2004; Yooshik and Uysal, 2005; Guzman et al., 2006; Correia et al., 2007 Hanoquin & Lam, 1999; Bansal and Eiselt, 2004; Beerli & Martin, 2004; Yooshik and Uysal, 2005; Guzman et al., 2006; Jang & Wu, 2006; Correia et al., 2007; Hsu Tsai,and Wu, 2009; Hung & Petrick, 2011 Jang & Wu, 2006; Hung & Petrick, 2011 Yooshik and Uysal, 2005 Hanoquin & Lam, 1999; Kim et al., 2003; Yooshik and Uysal, 2005; Guzman et al., 2006; Jang & Wu, 2006; Hung & Petrick, 2011; Correia et al., 2007 Recommended by interviewees

2345-

Being free to act as one feels Health and fitness Novelty seeking Adventure seeking

6- Having fun 7- Meeting different cultures and life styles 8- Self-esteem and social recognition 9- Rediscovering oneself 10-Spending time with one's family and friends 11-Developing close friendships 12-Meeting people with similar interests

168

Appendix 3. Questionnaire PART 1 - profile questions

N. Paker, C.A. Vural / Tourism Management 56 (2016) 156e171

N. Paker, C.A. Vural / Tourism Management 56 (2016) 156e171

Part 4 - additional comments

Part 2- expectations for marinas Please evaluate the importance of the following marina features.

1-Not important at all 5-very important

1

2

3

4

Location nearby the city center Closeness to airport Easy access to the destination Easy access to the marina Closeness to one's residence Prompt contact facilities to emergency points (fire station, ambulance etc.) 7- Closeness to transit and/or trip routes 8- Closeness to the other attractive destinations 9- Cultural and historical resources at the destination 10- Weather conditions (Wind, temperature, wave characteristics etc.) 11- Natural attractiveness 12- Beaches for swimming and sun tanning 13- Being quiet and not overcrowded 14-Variety of shopping facilities 15- Variety of restaurants and cafeterias 16- Events (Race, concert, festival, art exhibitions etc.) 17- Individual activity opportunities (Library, hobby gardens etc.) 18- Night life 19- Having appropriate social environment for yacht crews 20- Sports facilities (fitness center, water sports etc.) 21- Accommodation facilities (Hotel, pension etc.) 22- Cleanliness and hygiene conditions 23- Having blue flag 24- Service quality (Berthing, mooring, front office etc.) 25- Infrastructure quality (Fuel, water, electricity, toilet, laundry, internet etc.) 26- Having a well-equipped dry dock 27- Staff attitude towards marina customers 28- Local community attitudes towards tourists 29- Safety (Fire, sea traffic etc.) 30- Security (Against external threats) 31- Information and tourist services 32- Environmental friendliness 33- Image and popularity of the marina 34- 5 golden anchors 35- Unique and interesting culture of local people 36- Similar culture of local residence 37- Price policy 38- Being luxurious

References

Part 3-individual motivations Please evaluate the importance of the following motivation factors encouraging you for traveling to marinas.

1- Escape from routine 2- Being free to act as one feels 3- Health and fitness 4- Novelty seeking 5- Adventure seeking 6- Having fun 7- Meeting different cultures and life styles 8- Self-esteem and social recognition 9- Rediscovering oneself 10- Spending time with one's family and friends 11- Developing close friendships 12- Meeting people with similar interests

1

Please feel free to comment on any other marina features and individual motivation factors you think that should be included in this questionnaire.

5

123456-

1-Not important at all 5-very important

169

2

3

4

5

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Neslihan Paker is a lecturer at Transportation Services Department of Yas¸ar University Vocational School. She is also a PhD candidate at Yas¸ar University, Institute of Social Sciences, Department of Business Administration. She has an MBA degree from Yas¸ar University and industrial engineering undergraduate degree from Dokuz Eylül University, Engineering Faculty. She worked for the chemistry industry from 1995 to 2014. Her research interests include marketing, marine tourism, international marketing, clustering studies.

N. Paker, C.A. Vural / Tourism Management 56 (2016) 156e171 Ceren Altuntas¸ Vural is an Assistant Professor at International Trade Department of Dokuz Eylul University. She had her master's degree in Total Quality Management and PhD in Business Administration at Dokuz Eylul University. She worked for the transportation and logistics industry from 2002 to 2009. Afterwards she continued her scholarly career at Yasar University's Department of Transportation Services and Department of International

171 Logistics Management. Her research interests include industrial marketing, sustainability, ethics, corporate social responsibility, supply chain management, logistics and logistics centers. Ceren Altuntas¸ Vural has published in journals such as European Management Journal, International Marketing Review and International Journal of Logistics Research and Applications.