Between Leisure and Business: A Cluster Analysis of Golf Tourism in Spain

Abstract

In this study, the researchers aim to analyse the motivations of active golf tourists in Spain using the AFE-CFA-Cluster methodology. To this end, a survey was conducted in Spain amongst 381 players, both in person and online, who stated that they had undertaken one golf trip at least once in their lives. The survey consisted of three sections of questions: the first concerned preferences regarding both the destination and the golf courses; the second comprised 20 questions relating to motivations, divided into five categories: business opportunities, financial benefits, escape and relaxation, learning and challenge, and social interaction and camaraderie; and, finally, the third section focused on socio-demographic aspects. The results obtained from the exploratory factor analysis, which were subsequently confirmed by confirmatory factor analysis, revealed the composition of four motivational constructs: business opportunity, financial savings, escape and relaxation, and learning and challenge, resulting in a total of five homogeneous groups of golf tourists: experiential golfers, wellness-oriented golfers, multifunctional golfers, low-involvement golfers and learning-oriented golfers. These results may be useful for companies in the sector and marketing managers in defining the various existing segments of golf tourists and applying specific marketing strategies for each.

1. Introduction

Golf is a sport that has grown significantly in recent years; according to the Royal & Ancient (2025) report and the National Golf Foundation (2025) participation report, the number of golfers worldwide in 2024 exceeded 144 million people. Excluding the US and Mexico, the number of golfers over the last 8 years has risen from 66.6 million in 2016 to over 108 million, representing an increase of 62.16%. This increase is partly due to the introduction of new forms of play that make the sport more accessible to all types of players, such as golf simulators or pitch-and-putt courses (European Golf Association, 2025; Royal & Ancient, 2025). Furthermore, as indicated by J.-H. Lee et al. (2022), rising income levels have influenced people’s outlook on life, and the increase in leisure time has influenced people’s perception of sport. In the case of Spain, the number of federation golf licences has almost doubled since the start of the century, reaching 305,603 licences by early 2025 (Real Federación Española de Golf, 2025).

The golf tourism industry generates significant revenue and acts as an economic driver for host regions, being considered one of the largest sports-related tourism industries (Readman, 2012). For example, in the case of Spain, it is estimated that golf tourists visiting the country in 2023 generated a total economic impact of €14,152,002 (Real Federación Española de Golf, 2024).

Even so, the economic impact is not the only positive factor associated with golf tourism. In countries such as Spain, where the sun-and-beach model accounts for the majority of foreign tourist arrivals between May and September, golf tourism acts as a season-balancing factor, as the peak season for this type of tourism falls between March and April and October and November (García, 2021; Vadell et al., 2005). In this regard, the development of golf tourism helps to break the traditional seasonality that overwhelms tourist destinations during the summer months and serves as a perfect complement to sun and beach tourism (Babinger, 2012; Garau-Vadell & de Borja-Solé, 2008).

Golf tourism has gone hand in hand with the growth of the sport; however, there is no consensus in the scientific literature regarding its definition. According to Ramírez-Hurtado and Berbel-Pineda (2015), it can be defined as any trip where the primary intention is to play golf. This definition is in line with that provided by S. S. Kim et al. (2008), who define international golf tourism as trips of more than one night to foreign destinations where golf is played as the main tourist activity to fulfil the motivations for the trip. Alternatively, it can also be considered, as is the case with sports tourism, that golf tourism is a tourism pattern consisting of experiencing and witnessing events related to the sport (J.-H. Lee et al., 2022). Along the same lines, Humphreys (2010) defines it as travelling away from home to participate in or observe the sport of golf, or to visit attractions associated with golf. Given the above definitions, golf tourism can be defined as any trip of at least one night’s duration, in which playing golf or, failing that, attending golf-related events is a motivation—whether secondary or primary—for the trip. This definition would encompass the four types of sports tourism outlined by Robinson and Gammon (2004) depending on whether sport is the primary driver of the trip or a secondary incentive, whether it is a significant element of the holiday or merely an occasional activity, and whether it has a competitive or purely recreational focus.

Taking as a reference people residing in Spain regardless of their nationality and using motivation theory, this study aims to segment active golf tourists according to their motivations for taking a golf trip, in order to subsequently analyse the factors most valued by each tourist segment and their demographic characteristics, thereby helping management companies to create a profile of the target tourist towards whom to direct their marketing efforts. Through this study, the researchers aim to contribute a motivational framework to the existing literature on the study of motivations in active sports tourism, and more specifically to active golf tourism, given the scarcity of relevant research.

2. Literature Review

2.1. Sport Tourism

Sports tourism, as defined by Robinson and Gammon (2004), has two main aspects: on the one hand, sports tourism, which encompasses those who travel with sport as the primary motivation for the trip, as opposed to sports-related tourism, which refers to travellers for whom sport is a secondary activity. However, this definition has evolved over time; in their attempt to conceptualise sports tourism from a behavioural perspective, Weed (2005) define sports tourism as a synergistic phenomenon and criticise its definition based on the simple relationship between sport and tourism without taking into account the unique interaction between the activity, the people and the place.

As for its classification, Gibson (1998) distinguishes between event sports tourists, who are those who travel to attend a sporting event; active sports tourists, referring to people who travel to participate in sports; and nostalgic sports tourists, who travel to visit sports museums, famous sports venues and sports-themed cruises. However, just as with the term ‘sports tourism’, this classification has also evolved over time. In their work, Ramshaw and Gammon (2005) develop the concept of sports heritage, which they argue provides a better way of understanding sports tourism behaviour than nostalgia, defining it as one way—but not the only way—of engaging with the past. Subsequently, Weed and Bull (2012) establish that, in addition to active and passive participation, there is also a vicarious participation that encompasses the behaviours of nostalgia and heritage, incorporating the passionate behaviours of spectator sports tourists, who are anything but passive.

Currently, there is a disparity regarding the conceptualisation of sports tourism, as well as the classification of sports tourists, with numerous studies in the literature attempting to provide a definition without taking into account the evolution that sports tourism has undergone as a concept over time. Weed (2005) refers to this phenomenon in his work, drawing an analogy with a brick factory inspired by the work of Forscher (1963), in which he expresses his concern about the mass and sometimes haphazard production of works in the social sciences (bricks) that prevent the construction of a solid and coherent conceptual framework (building).

2.2. Motivations in Tourism and Active Sports Tourism

2.2.1. Motivations in Tourism

The basic theory of motivation describes a dynamic process involving internal psychological factors (needs, desires and goals) that generate an uncomfortable level of tension in the individual’s mind and body. These internal needs and the resulting tension lead to actions designed to release that tension, thereby satisfying the needs (Fodness, 1994). According to Gnoth (1997), motivations are a collective term for processes and effects with common parameters: in a particular situation, a person chooses a certain behaviour because of its expected outcomes. From a functional perspective, these internal needs and the resulting tension give rise to attitudes and, ultimately, actions based on those attitudes, designed to release the tension and thereby satisfy personal needs and goals (Fodness, 1994; Middleton & Clarke, 2012). Iso-Ahola (1980) uses the analogy of an iceberg to illustrate the complexity of studying tourists’ motivations, stating that the motivations of which tourists are aware represent only the visible part of the iceberg, whilst the vast majority lie beneath the water and are sometimes unidentifiable even to the tourists themselves—such as personality traits acquired in childhood, hence the complexity of analysing motivations.

Dann (1977) distinguishes between two main motivations that drive people to travel: anomie, which relates to the needs for escape and social interaction, and personal development linked to the ego and people’s need to feel valued. Likewise, there are ‘push’ factors, such as the need to disconnect or escape from everyday life, and ‘pull’ factors related to the tourism offering and the characteristics of the destination, such as the climate or the range of leisure activities (Dann, 1977).

For their part, Crompton (1979) identified nine motivations that drive a person to undertake a leisure trip: escape from an environment perceived as mundane, exploration and self-evaluation, relaxation, prestige, regression, strengthening of family relationships, facilitation of social interaction, novelty and education. The theory of leisure and free time suggests that motivations for participating in recreational activities can be classified as physical, social, psychological and emotional, intellectual and spiritual (McLean & Hurd, 2011). Robinson and Gammon (2004), in their attempt to simplify the study of sports tourists’ motivations, classified them as primary and secondary. Primary motivations refer to the intention to participate in sports as the main reason for the trip, whilst secondary motivations are related to other characteristics of the environment, such as the climate, the range of restaurants in the area or the quality of the golf courses, where applicable. In this way, secondary motivations act as elements that enrich the primary motivations.

2.2.2. Motivations in Active Sport Tourism

The study of sports tourists’ motivations is a highly complex field of research, given that each type of sports tourist will have different motivations, which also evolve over time and depending on the type of sport (Robinson & Gammon, 2004; Wann et al., 2008). In the literature on sports tourism, studies on sports tourism at events predominate (Weed, 2005); conversely, the study of motivations for active sports tourism has been based mainly on outdoor sports such as skiing (Bichler & Pikkemaat, 2021) and high-altitude tourism (Hodeck & Hovemann, 2018), cycling (Perić et al., 2019) or golf (Boukas & Ziakas, 2013; J. H. Kim & Ritchie, 2012). Furthermore, previous studies on the motivations of active sports tourists have categorised their work into winter sports and summer sports. Given that these studies analyse various types of sports and destinations and employ different methodological approaches, it is almost impossible to compare the individual results with one another (Hodeck & Hovemann, 2018). The first group encompasses active sports tourists involved in skiing and high-altitude mountaineering, whilst the second group mainly encompasses sports such as mountaineering, golf, water sports and cycling.

Dolnicar and Leisch (2003) categorise winter sports tourists in Austria according to their travel motivations for choosing a destination, identifying a total of seven clusters; the most highly valued motivations include the climate, keeping fit, accessibility, the surroundings and the amount of snow. Hodeck and Hovemann (2018), meanwhile, compare the motivations of winter and summer sports tourists visiting the Metallgebirge (Germany); a total of three groups of mountain sports tourists were identified, and the motivational factors under study included those related to health, enjoying the surroundings, relationships with friends and family, and relaxation. In their work Visintin et al. (2026) analyse the behaviour of mountain tourists in the Italian Alps through motivations such as the presence of mountain landscapes, the possibility of engaging in other activities, or health-related factors. In this regard, the main motivation was psychological well-being, followed by physical well-being and the site’s culinary specialities. Bichler and Pikkemaat (2021) segment urban ski tourists according to ‘push’ motivations such as escape and relaxation, family time or achievement-related factors; furthermore, they utilise ‘pull’ motivations such as the natural landscape, urban aspects like architecture, or basic factors such as hospitality and the safety of the area.

As regards active summer sports tourists, there is also little research on the study of their motivations (Hodeck & Hovemann, 2018). Among the most recent studies, one by Eskelinen et al. (2025) stands out, in which they segment disc golf tourists using push motivations such as the search for tranquillity, the need to spend time with family or friends, or improving skills; and pull motivations such as the possibility of playing several courses on the same trip, accommodation facilities for the whole family, or the natural beauty of the surroundings. Meanwhile, Perić et al. (2019) segment tourists engaged in mountain running, mountain biking, cross-country skiing and sport fishing using seven motivational constructs: enjoyment, physical appearance, competition, socialisation, experience with nature, improving skills and physical sport.

Regarding the motivations of active mountaineering tourists, the works of Dickson and Dolnicar (2006) and Woratschek et al. (2007) stand out. Dickson and Dolnicar (2006) use exploratory factor analysis and cluster analysis to analyse and segment active sports tourists on Mount Kosciuszko (Australia), defining a total of six homogeneous groups of tourists based on their travel motivations. Meanwhile, Woratschek et al. (2007) identified four segments with different motivations using cluster analysis. Furthermore, Dolnicar and Fluker (2003) segmented surfing tourists into six homogeneous groups based on their travel motivations and preferences, using an analysis of 430 respondents. Similarly, Reynolds (2012) interviewed 347 surfers from the south-eastern United States and identified three groups of sports tourists based on their motivations. In the case of golf, only two studies have been found that examine the motivations of active tourists: on the one hand, the study by Boukas and Ziakas (2013), which analyses the motivations for visiting Cyprus among 100 golf tourists; and on the other, the study by J. H. Kim and Ritchie (2012), which segments Korean golf tourists according to their motivations using exploratory factor analysis and cluster analysis, resulting in three groups of active golf tourists. Terzić et al. (2021) identify three homogeneous groups of active sports tourists based on their personal values and motivations, drawing on a sample of over 40,000 respondents from 21 different countries; the criterion for inclusion was participation in a sport on a weekly basis, without specifying whether these were winter or summer sports.

The multitude of different motivational variables underscores the complexity of motivations in sports tourism, which can be countless depending on the type of tourism in question; for example, the motivations of sports tourists attending an event differ from those of sports volunteers and active sports tourists. These combined collective motives illustrate that, at present, it is unrealistic to identify and link the almost countless motivational variables found in both sport and tourism (Robinson & Gammon, 2004).

Through this research, the researchers aim to provide a motivational framework for analysing the reasons that drive golf tourists to undertake an active sports holiday in Spain—a framework that has not previously existed in the scientific literature.

Table 1. Studies on segmentation based on motivations in active sports tourism.

Authors (Year)	Title	Target Tourism	Number of Segments	Method Used
Dolnicar and Leisch (2003)	Winter tourist segments in Austria: Identifying stable holiday styles using bagged clustering techniques	Active winter sports tourism in Austria	7	Cluster analysis	Active winter sports tourism
Hodeck and Hovemann (2018)	Motivation of active sport tourists in a German highland destination—a cross-seasonal comparison	Active sports tourists in the Metallic Mountains (Germany)	3	Exploratory factor analysis (EFA) and cluster analysis
Bichler and Pikkemaat (2021)	Winter sports tourism to urban destinations: Identifying potential and comparing motivational differences across skier groups	Ski tourists in urban destinations	3	Exploratory factor analysis and cluster analysis
Visintin et al. (2026)	Visitor segmentation in alpine tourism: Evidence from a survey-based cluster analysis in northern Italy	Alpine tourism (hiking)	3	Cluster analysis
Dolnicar and Fluker (2003)	Behavioural market segments among surf tourists: Investigating past destination choice	Active surfing tourism	6	Cluster analysis	Active summer sports tourism
Dickson and Dolnicar (2006)	Ascending Mount Kosciusko: An exploration of motivational patterns	Climbing tourism on Mount Kosciusko (Australia)	6	Exploratory factor analysis (EFA) and cluster analysis
Woratschek et al. (2007)	Motivations of sports tourists: An empirical analysis in several European rock climbing regions	Climbing tourism	4	Cluster analysis
J. H. Kim and Ritchie (2012)	Motivation-based typology: An empirical study of golf tourists	Golf tourism	3	Exploratory factor analysis (EFA), cluster analysis and multiple discriminant analysis
Reynolds (2012)	Surfing as adventure travel: Motivations and lifestyles	Active surfing tourists in the south-east of the United States	3	Exploratory factor analysis (EFA)
Boukas and Ziakas (2013)	Golf tourist motivation and sustainable development: A marketing management approach for promoting responsible golf tourism in Cyprus	Active golf tourists in Cyprus	4	Chi-square and Kruskal–Wallis tests
Eskelinen et al. (2025)	Motivations for domestic overnight travel by Finnish disc golfers: a serious-leisure perspective	Disc golf tourism	5	Cluster analysis

shows the studies identified by the authors on segmentation based on motivations in active winter and summer sports tourism.

2.3. Segmentation in Golf Tourism

The motivations driving golf tourists to undertake their trips can be very varied; for example, a tourist travelling for business or social interaction may have different behavioural patterns and preferences to those travelling to improve their skills (J. H. Kim & Ritchie, 2012).

This information is extremely useful for companies in the sector to understand that, through segmentation, tourists can be grouped into homogeneous and heterogeneous groups, thereby gaining a better understanding of the different customer segments and enabling the implementation of specialised marketing strategies (Andreu et al., 2006; C.-K. Lee et al., 2006; Cha et al., 1995; Dann, 1977; Fodness, 1994; Ramos-Ruiz et al., 2026). If golf course and tourism management companies understand their customers’ preferences and motivations, they can promote the positive aspects, thereby increasing tourist satisfaction and, in turn, the likelihood of repeat visits and recommendations to friends with similar interests (Ramírez-Hurtado & Berbel-Pineda, 2015). Segmentation within the field of sports tourism has already been carried out based on different sports and destinations, although these studies can hardly be compared with one another due to differing research objectives and designs (Hodeck & Hovemann, 2018).

Golf tourism, conceived as a sub-segment of sports tourism, has been of particular relevance in recent years given its size and value (Hudson & Hudson, 2014). However, there are few studies in the scientific literature that segment golf tourists according to their motivations (Boukas & Ziakas, 2013; J. H. Kim & Ritchie, 2012).

Among the researchers who have studied the different types of tourists in golf tourism, the work of J. H. Kim and Ritchie (2012) stands out; this study uses motivations to identify different segments of Korean golf tourists, distinguishing between intensive, multimodal and accompanying golfers. Ramírez-Hurtado and Berbel-Pineda (2015) segment transoceanic golf tourists travelling to Spain based on trip and personal characteristics. They have also been segmented according to their level of specialisation, distinguishing between low, medium and high specialisation (S. S. Kim et al., 2008). S. Kim et al. (2001) segment golf tourists according to their attitude towards the availability of food and drink services on the golf course. Gibson and Pennington-Gray (2005) use role theory to identify different segments of golf tourists. For their part, Correia et al. (2009) analyse motivations, perceptions and expectations as components of the destination image, grouping golf tourists visiting the Algarve into three distinct segments. Finally, Boukas and Ziakas (2013) identify four segments of golf tourists based on their motivations for visiting Cyprus as an active golf destination.

These studies indicate that golf tourists are heterogeneous but can be grouped according to their demographic variables, attitudes, behaviours or level of specialisation. In this research, the concept of motivation is presented as a key for understanding the different characteristics of golf tourists visiting Spain. Through this study, and given the scarcity of research addressing the motivations of active sports tourists in summer sports and, more specifically, in golf tourism, the researchers aim to contribute to the development of a motivational framework for the segmentation of active golf tourists. They also aim to address the current gap in research into the motivations of golf tourists in Spain and their subsequent segmentation into homogeneous groups, providing golf course and tourism management companies with profiles featuring different characteristics to aid in the development of specific marketing strategies.

Table 2. Studies on segmentation in active golf tourism.

Authors (Year)	Title	Target Tourism	Number of Segments	Segmentation Criteria
S. Kim et al. (2001)	Segmenting Golfers by Their Attitudes Toward Food and Beverage Service Available During Play	Golfers in the Dallas, Texas area	3	Segmentation based on golfers’ attitudes towards the availability of food and beverage service on the course
Gibson and Pennington-Gray (2005)	Insights from Role Theory: Understanding Golf Tourism	Canadian active golf tourists	4	Role theory
S. S. Kim et al. (2008)	Segmenting overseas golf tourists by the concept of specialisation	Korean active golf tourists abroad	3	Concept of specialisation
Correia et al. (2009)	Bridging perceived destination image and market segmentation—An application to golf tourism	Golf tourists in Algarve (Portugal)	3	Motivations, expectations, and perceptions
J. H. Kim and Ritchie (2012)	Motivation-based typology: An empirical study of golf tourists	Active Korean golf tourists	3	Motivation theory
Boukas and Ziakas (2013)	Golf tourist motivation and sustainable development: A marketing management approach for promoting responsible golf tourism in Cyprus	Active golf tourists in Cyprus	4	Theory of motivations
Ramírez-Hurtado and Berbel-Pineda (2015)	Identification of segments for overseas tourists playing golf in Spain: A latent class approach	Overseas golf tourists travelling to Spain	4	Travel preferences and personal characteristics

shows the studies on segmentation in active golf tourism.

2.4. Motivational Constructs

2.4.1. Business Opportunity

The construct of ‘business opportunity’ refers to the willingness of golf tourists to develop business relationships during their trip, whether with clients or other businesspeople. This motivational variable has previously been used in the study by J. H. Kim and Ritchie (2012) to segment golf tourists based on their motivations. For their part, Barros et al. (2010) use the motivational factor ‘business opportunity’ to study the duration of golf tourists’ trips in the Algarve.

2.4.2. Financial Savings

The motivational construct of financial benefit refers to the possibility of playing golf more cheaply at the destination than at the place of origin. This is common in Southeast Asian countries such as South Korea or Japan, where, either due to the cold weather or the high cost of golf rounds, golfers travel to countries in the region such as Thailand or Malaysia. Furthermore, their own government allocates significant investment in advertising to attract golf tourists, capitalising on the milder climate and lower prices for playing golf (Cham et al., 2022; S.-Y. Lee & Lee, 2022). In their paper S. S. Kim et al. (2005) analyse the preferences of Korean golf tourists for overseas destinations, using cost savings as a selection variable for the golf destination. Furthermore, in studies on active sports tourism, expenditure has been used to categorise different groups of active sports tourists, distinguishing those groups with higher expenditure from those that tend to spend less (Barros et al., 2010; Hodeck et al., 2018).

2.4.3. Escape and Relaxation

This motivational variable has traditionally been used to explain the behaviour of tourists who seek to travel in order to disconnect from their daily routine (Dann, 1977). Likewise, within the context of sports tourism, it has been analysed by numerous researchers for both active sports tourists and spectators at sporting events (Bason, 2023; Bichler & Pikkemaat, 2021; Carvache-Franco et al., 2025; Dickson & Dolnicar, 2006; J. H. Kim & Ritchie, 2012).

2.4.4. Learning and Challenge

This motivational construct is directly related to the category of motivations linked to the ego and self-esteem (Dann, 1977). Its analysis in numerous studies on sports tourism demonstrates the validity of the construct (Bichler & Pikkemaat, 2021; Dickson & Dolnicar, 2006; Eskelinen et al., 2025; J. H. Kim & Ritchie, 2012; Perić et al., 2019; Robinson & Gammon, 2004).

3. Methodology

Firstly, in developing the survey items, reference was made to other studies on golf tourism (Cham et al., 2022; J.-H. Lee et al., 2022; J. H. Kim & Ritchie, 2012).

Table 3. Survey sections by reference article.

Preference Factors
Climate	Cham et al. (2022)
Accessibility
Safety and security
Prior knowledge of the area
Variety of courses in the area
Availability of other leisure activities
The area’s fame and reputation
Range of hotels and restaurants in the area
Preference factors regarding the golf course
Service received	S.-Y. Lee and Lee (2022)
Quality of the course
Restaurant
Green fee
Practice facilities
Course management
Accommodation
Accessibility
Motivational factors
Business opportunity	J. H. Kim and Ritchie (2012)
I like to talk business when I play golf
I could achieve business objectives by playing golf
I enjoy entertaining clients/partners through golf
Benefits
I can play more rounds of golf more cheaply
I can play without needing a membership
I can travel more cheaply than playing golf at home
I can avoid bad weather
I can enjoy multi-purpose trips during my golf holidays
Learning and challenge
I want to play on a highly regarded course
I want to play in golf tournament qualifiers
I like to improve my golf skills and knowledge
I enjoy taking part in physical activities
Escape/relaxation
I want to escape the domestic difficulties of golf resorts
I want to escape the crowds
I want to escape the elitist image of golf
I want to escape the daily grind to watch golf tournaments
Social interaction/fellowship
It could improve relationships with friends
I enjoy building relationships with members of the local club
I like travelling with my family
Visiting family or friends

shows which sections were adopted from each article.

The first part of the survey consists of questions relating to tourists’ preferences, which in turn can be divided into two sub-sections: the first seeks to identify the most highly valued preferences at destination locations, whilst the second aims to clarify the preferences of golf tourists at golf courses in order to identify the most highly valued factors. For the analysis of motivations, a total of 20 questions or items were formulated using a Likert scale from 1 to 7, where 1 means that the statement does not motivate them at all and 7 means that it motivates them a great deal. These have in turn been divided into 5 sub-sections based on the nature of the motivations, structured as follows: Business opportunities, comprising 3 items; Benefits, comprising 5 items; Learning and challenges, comprising 4 items; Escape and relaxation, comprising 4 items; Social interaction and kinship, comprising 4 items. The third section of the survey analyses socio-demographic aspects such as the respondent’s age, gender, profession, level of education or income.

The survey was conducted in collaboration with staff from the University of Córdoba, the University of Granada and the University of León. It underwent several rounds of review and testing before being finalised. The survey was then uploaded via an online survey platform, and a poster with its corresponding QR code was created so that it could be completed using any electronic device with internet access. It was also promoted via the Facebook Business social media platform, thereby reaching a wider audience.

3.1. Data Collection

The questionnaire began to be distributed in February 2023 via a convenience sample, conducted in person at golf courses on the Costa del Sol, primarily the Miguel Ángel Jiménez Golf Academy and El Chaparral Golf Club, specialist shops in the area, travel agencies specialising in golf, WhatsApp groups for golfers, and other organisations such as the Royal Andalusian Golf Federation, the Royal Spanish Golf Federation and the Spanish Association of Golf Courses. To ensure greater representativeness of the sample, random days were selected, including both weekends and weekdays. Subsequently, it was disseminated via the Facebook Business social media platform, which ensured that respondents came from across the whole country.

The survey closed on 6 June 2023 with a total of 721 responses. After discarding those that were incomplete, the number of completed questionnaires stood at 381, of which 157 were collected in person and 224 online.

3.2. Data Processing and Analysis

Data analysis was carried out using the statistical analysis tool IBM SPSS Statistics 30.0 for exploratory factor analysis (EFA) and cluster analysis. For confirmatory factor analysis (CFA), the statistical analysis tool Rstudio 4.2.0 and the packages Lavaan, Semplot and Semtools were used.

3.3. Exploratory Factor Analysis (EFA)

Firstly, the normality of the motivational constructs was tested using the Kolmogorov–Smirnov and Shapiro-Wilks normality tests (Kolmogorov, 1933; Shapiro & Wilk, 1965; Smirnov, 1948). Subsequently, an exploratory factor analysis (EFA) of principal components was conducted to explore more precisely the underlying dimensions, constructs or latent variables of the observed variables (Mavrou, 2015). Initially, the suitability of the data for this analysis was verified, yielding values greater than 0.7 on the Kaiser-Meyer-Olkin index (Romero & Mora, 2020) and the correlation between the variables was supported by Bartlett’s sphericity test with p < 0.001, being less than 0.05 (J. F. Hair et al., 2010; Romero & Mora, 2020). The reliability of the results was verified using Cronbach’s alpha (α) (Cronbach, 1951) and McDonald’s omega (ω) (McDonald, 1989); all results obtained a score above the recommended threshold of 0.7 (Nunnally & Bernstein, 1994). Meanwhile, the sample size complies with the recommended parameters of between 300 and 400 values (J. F. Hair et al., 2006). According to Mavrou (2015) the recommended number of observations per variable is between 15 and 20, a figure far exceeded in this study.

The ratio of the number of variables per factor must meet the minimum of 3:1 (Mavrou, 2015), with a recommendation to interpret only well-identified common factors and to reduce the number of factors if some are represented by only one or two variables with high saturation (Velicer & Fava, 1998).

To determine the number of factors, the Gutman-Kaiser rule or latent root criterion was followed, using only those factors that have obtained eigenvalues greater than 1 (J. F. Hair et al., 2006; Mavrou, 2015). According to J. F. Hair et al. (2006), this method may not yield reliable results for fewer than 20 variables or may limit the explanatory power of the factor solution; however, in the case of the present study, it is appropriate as there are 20 variables.

According to some authors, the values of factor loadings may vary depending on the sample. In this vein, J. F. Hair et al. (2006) argue that for a sample of 70 and 75 individuals, the factor loadings should be at least 0.6 and 0.65 respectively. Furthermore, it is recommended to use only those factor loadings with an absolute value greater than 0.4 (Field, 2009). It could be concluded that the identification of a factor through three variables that present loadings of 0.60 (or ideally loadings greater than 0.71) on that factor would be sufficient to assume that these variables are good indicators of the latent construct of interest (Mavrou, 2015). In the case of the present study, only two variables have shown factor loadings below 0.71 but with very close values (0.698 and 0.708).

With regard to the percentage of variance explained, and following J. F. Hair et al. (2006), the percentage of variance explained in the social sciences should be greater than 60%. In this study, a level of explained variance of 69.44% was obtained, very similar to other studies conducted in golf tourism (J. H. Kim & Ritchie, 2012).

To arrive at the final data set, several iterations were carried out, starting with a total of 20 variables and progressively eliminating those that did not comply with any of the principles established in the preceding paragraphs, until a total of 12 remained. After each new analysis, once a variable had been removed, a reliability test was carried out and the results were compared with the original results (with 20 variables) to verify the improvement in the fit.

3.4. Confirmatory Factor Analysis (CFA)

To validate the latent structure identified in the exploratory factor analysis (EFA), a confirmatory factor analysis (CFA) was conducted using the statistical software RStudio and the packages Lavaan, Semplot and Semtools. Given the non-normality of the variables, the weighted least squares method with mean and variance adjustment (WLSMV) was chosen for the analysis.

The average variance extracted (AVE) was used to measure the level of variance explained by each variable in each of the factors. Following (Fornell & Larcker, 1981), values below 0.5 would not be acceptable as too much information is lost from each variable (Fornell & Larcker, 1981). Furthermore, to analyse the reliability of the construct, the composite reliability (CR) was calculated; values greater than 0.7 are considered acceptable and those greater than 0.8 are considered good (Nunnally & Bernstein, 1994).

Likewise, the factor loadings must be greater than 0.4 to ensure the stability of the construct (J. F. Hair et al., 2010). In the present study, AVE values greater than 0.5 were obtained for each construct and factor loadings greater than 0.4 for all variables, thus confirming the suitability of the variables.

To measure the model’s fit, absolute fit indices were used, such as the Root Mean Square Error of Approximation (RMSEA), which indicates that values below 0.5 represent a good fit and those between 0.5 and 0.8 an acceptable fit (Hu & Bentler, 1999; Romero & Mora, 2020). The Goodness-of-Fit Index (GFI) belongs to the same group as the previous one, and values above 0.9 indicate a good fit (Romero & Mora, 2020).

The Comparative Fit Index (CFI) was calculated; values above 0.9 are considered acceptable and those above 0.95 are considered good (J. Hair et al., 2019). The Tucker–Lewis Index (TLI) considers values above 0.9 to be acceptable (J. F. Hair et al., 2010). Both belong to the group of incremental indicators.

Finally, the standardised root mean square residual (SRMR) indicator, known as the parsimony-corrected measure, was calculated; values below 0.8 indicate a good fit (Martínez Ávila, 2021).

Finally, the chi-square test was calculated to analyse the associations between the categorical variables included in the study and the resulting clusters; to measure the effect size of each demographic variable on the clusters, Cramer’s V was used, a robust measure of association commonly employed to assess the strength of relationships between nominal variables (Cohen, 2013). For interpretation, values between 0 and 0.3 are considered to indicate a low effect, between 0.3 and 0.5 a medium effect, and above 0.5 a high effect.

3.5. Cluster Analysis

Finally, a non-hierarchical cluster analysis is carried out using the factors resulting from the exploratory and confirmatory factor analyses, standardised using Ward’s method and Euclidean squared distance, to obtain the number of clusters into which the sample is divided and, in this way, to segment the group of respondents into homogeneous groups based on their motivational characteristics. This method has previously been used in sports tourism research (Ramos-Ruiz et al., 2026) and specifically in studies on golf tourism (J. H. Kim & Ritchie, 2012; Ramírez-Hurtado & Berbel-Pineda, 2015; S. S. Kim et al., 2008) demonstrating its usefulness in this context.

As there is no clear statistical rule for determining the optimal number of clusters, the researchers opted for a two-stage analysis. Firstly, using the dendrogram and the clustering history obtained via Ward’s method, it was found that the optimal number of clusters was 5, as the error increased significantly beyond that limit, with the dendrogram’s cut-off value set at 8 points. Once the number of clusters had been defined, a K-means clustering analysis was carried out, resulting in the identification of homogeneous groups of golf tourists based on their motivations. The robustness of the clusters was measured using the sum of squares of the clusters and the F-value provided by the ANOVA table, which shows the distance between the clusters, with the best values being those furthest from 1.

Finally, an analysis of the demographic characteristics of each cluster was carried out in order to better understand the specific features of each cluster, and they were subsequently labelled.

4. Results

4.1. The Demographic Profile

The demographic profile of the average golf tourist in Spain, according to the results obtained in this study, is a man aged between 46 and 60, retired, with a monthly income of over €3500 and a university education.

Table 4. The demographic profile.

Age			Gender
Under 30 years	15	3.9%	Female	81	21.3%
Between 31 and 45 years	51	13.4%	Male	298	78.2%
Between 46 and 65 years	227	59.6%	Non-binary	2	0.5%
66 years or older	88	23.1%
Occupation			Income level
Independent professional	55	14.4%	Less than 700 euros	4	1.0%
Civil servant	43	11.3%	Between 700 and 1000 euros	1	0.3%
Private company employee	107	28.1%	Between 1001 and 1500 euros	19	5.0%
Self-employed	34	8.9%	Between 1501 and 2500 euros	94	24.7%
Student	4	1.0%	Between 2501 and 3500	106	27.8%
Unemployed	5	1.3%	More than 3500	157	41.2%
Retired	129	33.9%
Household work	4	1.0%
Education
Primary education	7	1.8%
Secondary education	32	8.4%
Vocational training	58	15.2%
University degree	202	53.0%
Postgraduate degree	82	21.5%

shows the results obtained.

In terms of age, people aged between 46 and 65 stand out, representing 59.6% of the sample, followed by those over 65 at 23.1%. These results are consistent with studies conducted by J. H. Kim and Ritchie (2012), Cham et al. (2022) and S.-Y. Lee and Lee (2022) regarding the predominant group, but differ in the participation of younger people; this may be due to the specific characteristics of golf tourists in the country or the method of disseminating the survey; however, further research would be necessary to understand the causes in greater depth.

The gender ratio of respondents is approximately 4 to 1 in favour of men, who account for 78.2% of the total; women, with 81 cases, represent 21.3%; and two people who stated they did not identify with any gender represent 0.5% of the total respondents. These results are quite similar to those obtained in the research by S. S. Kim et al. (2008), J. H. Kim and Ritchie (2012), Cham et al. (2022) and the percentage of female Spanish licences according to their 2023 report (Real Federación Española de Golf, 2025). However, these do not align with the results obtained by S.-Y. Lee and Lee (2022), in which female participation, whilst not exceeding that of men, is significantly higher; this may be due to greater female participation in Korean golf compared to Spanish golf.

The predominant occupation among respondents is retired, accounting for over a third of the total sample and a total of 129 people (33.9%), followed by private sector employees with 107 respondents representing 28.1% of the sample. These are followed by self-employed professionals and civil servants, with 55 cases (14.4%) and 43 cases (11.3%) respectively. Finally, the least represented professions were the self-employed, accounting for 8.9% of the sample with 34 respondents; the unemployed, with 5 cases (1.3%); and students and homemakers, with 4 cases each, representing 1% of the total number of respondents. The high proportion of retired people does not align with the results obtained by S. S. Kim et al. (2008), J. H. Kim and Ritchie (2012), S.-Y. Lee and Lee (2022). This may be due to the high proportion of golf tourists from other countries who come to Spain to enjoy their retirement or to the inherent characteristics of the Spanish economy; however, further research would be needed to explore the possible causes in greater depth.

Looking at income levels, nearly half of the respondents reported earning more than €3500 per month, totalling 157 respondents (41.2%), followed by those who reported earning between €2501 and €3500 per month, totalling 106 people and representing 27.8% of the total sample. Next came the group of respondents who reported earning between €1501 and €2500 per month, comprising 94 respondents (24.7%); and finally, only 6.3% reported earning less than €1500 per month, comprising 24 cases. This high purchasing power is consistent with the results obtained by S. S. Kim et al. (2008).

Finally, regarding the educational level of the respondents, those with higher education stood out, totalling 202 cases (53%), whilst 82 cases (21.5%) of respondents reported having university and postgraduate qualifications respectively. Those who reported having vocational training totalled 58 cases, accounting for 15.2% of the sample; meanwhile, respondents with a secondary school education totalled 32 people (8.4%); and finally, only 1.8% (7 cases) had only primary school education. These results are consistent with the studies conducted by S. S. Kim et al. (2008).

4.2. Descriptive Analysis of Motivations

Table 5. Descriptive analysis of Motivations.

Motivation Group/Item	Standard Deviation	Mean		Cronbach’s Alpha (α)	Omega McDonald (ω)
Business Opportunity
MOT1. I like to discuss business when playing golf	1.613	2.197	2.597 1	0.857	0.851
MOT2. I could achieve business goals by playing golf	1.812	2.399
MOT3. I enjoy entertaining clients/partners through golf	2.111	3.194
Benefits
MOT4. I can play more rounds of golf at a lower cost	1.937	4.598	4.951	0.774	0.787
MOT5. I can play without needing a membership	1.876	5.089
MOT6. I can travel at lower cost than domestic golf	1.810	4.470
MOT7. I can avoid bad weather	1.752	5.171
MOT8. I can take multipurpose trips during golf vacations	1.504	5.428
Learning & Challenge
MOT9. I want to play on a highly reputable course	1.722	4.816	4.748	0.7	0.7
MOT10. I want to play in golf championship preliminaries	1.962	3.751
MOT11. I enjoy improving my golf skills and knowledge	1.380	5.675
MOT12. I like participating in physical activities	1.766	4.751
Escape/Relaxation
MOT13. I want to escape domestic golf booking difficulties	1.857	4.449	4.716	0.773	0.775
MOT14. I want to escape the crowds	1.742	5.084
MOT15. I want to escape the elitist view of golf	1.990	5.013
MOT16. I want to escape the routine to watch golf championships	1.867	4.318
Social Interaction & Kinship
MOT17. I could improve relationships with friends	1.706	5.063	4.955	0.656	0.685
MOT18. I like establishing relationships with local club members	1.902	4.444
MOT19. I like traveling with my family	1.621	5.522
MOT20. Visiting relatives or friends	1.869	4.790

¹ Mean value of the complete construct.

shows the descriptive statistics for the motivational variables studied and the reliability values measured by Cronbach’s alpha (α) and McDonald’s omega (ω).

The highest-rated motivations were MOT11: “I enjoy improving my golf skills and knowledge” and MOT19: “I enjoy travelling with my family”, with average scores of 5.675 and 5.52 respectively, making them the main drivers for golf tourists when planning a sports-focused trip. Conversely, motivations MOT1 “I like to talk business when I play golf” and MOT2 “I could achieve business objectives by playing golf” received the lowest scores, with average scores of 2.197 and 2.399 out of 7 respectively. If we focus on the groups of motivations, the one with the highest average score is “Social interaction and kinship” with 4.955 points out of 7, indicating that for golf tourists, travelling with family and friends, as well as meeting local people, are their main motivations. The ‘Business opportunities’ motivation group receives the lowest average score, with 2.597 out of 7, indicating that the majority of golf tourists in Spain do not view golf as a bridge for conducting business.

4.3. Exploratory Factor Analysis of Principal Components

The normality of the variables was tested using the Kolmogorov–Smirnov test, yielding a p-value <0.001 for all variables, thereby ruling out a normal distribution. Subsequently, the suitability of the variables for exploratory factor analysis was assessed using the KMO test and Bartlett’s sphericity test. The results are shown in

Table 6. Suitability Tests for EFA: KMO and Bartlett’s Sphericity.

Kaiser–Meyer–Olkin Measure		0.753
Bartlett’s Test of Sphericity:	χ2	1560.05
	gl	66
	p	<0.001

.

Once the suitability of the variables had been verified, an initial factor analysis was carried out using 20 variables, yielding a total explained variance of 60.335%; however, MOT18 (“I like building relationships with people at the local club”) did not exceed the threshold of eigenvalues greater than 1, and was therefore removed. The second principal component analysis was carried out with 19 variables, yielding an explained variance of 61.619%, an improvement on the previous results; however, factor loadings of less than 0.5 were obtained for the variables MOT17 “I could improve relationships with friends” and MOT8 “I can go on multi-purpose trips during my golf holidays”. The third principal component analysis was carried out with 17 variables, yielding a total explained variance of 64.622%. However, a factor loading close to 0.5 was obtained for MOT12 “I like to participate in physical activities”, so it was removed and the results compared. Performing the analysis with 16 variables yields a cumulative explained variance of 66.505%, improving on the previous results. We proceed to remove variable MOT16 “I want to escape the routine to watch golf tournaments” as it has factor loadings close to 0.5, and we perform the analysis again. The total cumulative variance increases to 68.539% with 15 variables; however, the variables MOT19 “I like travelling with my family” and MOT20 “Visiting family or friends” form a two-variable factor, failing to meet the minimum requirement of three variables, even though they have high factor loadings (Mavrou, 2015; Velicer & Fava, 1998). After removing these two variables and variable MOT7 “I can avoid bad weather” due to their factor loadings being close to 0.5, the final analysis yields a total of 4 factors and a cumulative total variance of 69.438% with the remaining 12 variables, which is higher than the recommended level of 60% (J. F. Hair et al., 2006).

The results obtained meet the conditions established for a good fit according to the established methodological criteria (J. F. Hair et al., 2010; Mavrou, 2015; Romero & Mora, 2020; Velicer & Fava, 1998).

Table 7. Exploratory factor analysis.

Factors	Items	Factor Loadings	Eigenvalues	Explained Variance %	AVE	CR
Business opportunity	MOT1	0.906	3.576	29.799	0.751	0.900
	MOT2	0.906
	MOT3	0.782
Economic benefit	MOT4	0.698	2.108	17.563	0.644	0.843
	MOT5	0.855
	MOT6	0.845
Learning and challenge	MOT9	0.794	1.342	11.182	0.639	0.841
	MOT10	0.741
	MOT11	0.708
Escape and relaxation	MOT13	0.775	1.307	10.894	0.560	0.792
	MOT14	0.87
	MOT15	0.747

shows the results obtained.

Of the four resulting factors, the constructs ‘business opportunity’, ‘learning and challenge’ and ‘escape and relaxation’ are consistent with the findings of previous research on golf tourism (J. H. Kim & Ritchie, 2012); however, the construct “financial savings” refers only to variables related to the economic benefits of travelling away from tourists’ place of residence, without including the variables “I can avoid bad weather” and “I can undertake multi-purpose trips during my golf holidays” as J. H. Kim and Ritchie (2012) do in their study.

Factor 1, “Business opportunity”, explains 29.799% of the variance and all the factor loadings of its variables are greater than 0.782. Meanwhile, Factor 2 “Financial savings” explains 17.563% of the cumulative variance and all the factor loadings of its variables exceed the 0.71 threshold proposed by Mavrou (2015) with the exception of variable MOT4 “I can play more rounds of golf more cheaply”, which has a factor loading of 0.698, very close to the indicated threshold. Factor 3, “Learning and challenge”, accounts for 11.182% of the explained variance and, as with Factor 2, all its variables have factor loadings above the 0.71 threshold, with the exception of one: MOT11, “I like to improve my golf skills and knowledge”, which has a value very close to this (0.708). Finally, factor 4, “Escape and relaxation”, accounts for 10.894% of the explained variance, and all variables have factor loadings above 0.71.

The exclusion from this study of motivational factors such as the importance of physical activity or wanting to travel to be with family and friends, as is done in other studies on golf tourism and active sports tourism, may lead to the conclusion that the motivations of active golf tourists are changing over time. This is a process that may occur (Robinson & Gammon, 2004); however, future research will need to investigate the motivations of active golf tourists through robust analysis in order to compare the results and verify whether the same conclusions hold over time or whether this is merely a sporadic phenomenon.

4.4. Confirmatory Factor Analysis

Once the exploratory factor analysis (EFA) has been carried out, confirmatory factor analysis (CFA) is performed using the weighted least squares method with mean and variance adjustment (WLSMW), which is widely used when variables do not follow a normal distribution. As shown in Table 4, the values for the average variance explained (AVE) and composite reliability (CR) are in all cases higher than the recommended thresholds of 0.5 and 0.7 respectively, which constitutes evidence of internal consistency and convergent validity (Fornell & Larcker, 1981).

Table 8. Adjustment indicators.

Adjustment Indicators
χ2	218.701
gl	48
p	0.0000
CFI	0.962
TLI	0.947
RMSEA	0.097
SRMR	0.071
GFI	0.99
RNI	0.962
IFI	0.985
GFI	0.99
NNFI	0.98

below shows the results obtained for the various fit indices.

The chi-square goodness-of-fit statistic χ² yields satisfactory results (χ² = 218.701; df = 48; p < 0.001), whilst the root mean square error of approximation (RMSEA) is slightly higher than recommended (RMSEA = 0.097). This may be because this indicator requires the distribution to be symmetric for samples ranging from 100 to 450 (Morata-Ramírez et al., 2015). However, although this poor result represents a limitation in terms of the model’s fit, it is offset by the other indicators, which suggest that the model fits the data correctly and is therefore satisfactory overall. Meanwhile, the standardised root mean square residual (SRMR) yielded adequate values (SRMR = 0.071). To conclude the discussion of absolute fit indices, the Goodness-of-Fit Index (GFI) yields a very satisfactory value (GFI = 0.99).

Regarding the incremental fit indices, the Comparative Fit Index (CFI) obtained a value higher than the recommended 0.95 (CFI = 0.962) to be considered a good fit according to J. Hair et al. (2019). The Tucker–Lewis Index (TLI) obtained a value higher than the threshold recommended as acceptable and very close to the threshold for a good fit (TLI = 0.947). Other incremental fit indices were additionally calculated to provide greater robustness to the model (NNFI = 0.98; RNI = 0.962; IFI = 0.985).

The standardised loadings of each factor for each variable (Figure 1) exceed the recommended values in all cases, validating the stability of the construct. In short, given the results obtained, it can be concluded that the confirmatory factor analysis (CFA) confirmed that the model fit is satisfactory and meets the thresholds recommended in Section 3.4.

4.5. Cluster Analysis

Once the factors have been defined using exploratory and confirmatory factor analysis (business opportunity, financial savings, learning and challenge, and escape and relaxation), standardised scores are obtained to carry out non-hierarchical cluster analysis, through which five clusters or homogeneous groups of golf tourists are identified.

A K-means cluster analysis is carried out to define the different clusters. The model results in the grouping of golf tourists into five distinct latent groups (experiential golfers, escape golfers, multifunctional golfers, low-involvement golfers and learning-oriented golfers). The clusters with the shortest distance between their centres, and therefore the most like one another, are cluster 4 ‘Low-involvement golfers’ and cluster 5 ‘Learning-oriented golfers’, with a distance of 1.815. Conversely, the clusters with the greatest difference between their centres and therefore the most dissimilar to one another are cluster 4, ‘Low-involvement golfers’, and cluster 3, ‘Multifunctional golfers’, with a distance of 3.6.

Table 9. Cluster analysis. ANOVA table.

Factors	Cluster		Error		F	Sig.
	Mean Square	df	Mean Square	df
(1) Business Opportunity	63.897	4	0.331	376	193.108	0.000
(2) Economic benefit	44.438	4	0.538	376	82.614	0.000
(3) Learning/Challenge	52.967	4	0.447	376	118.451	0.000
(4) Escape/relaxation	45.330	4	0.528	376	85.785	0.000

shows that all the defined dimensions are significant for grouping homogeneous clusters of golf tourists.

Table 10. Cluster analysis. Mean, standard deviation, z-score, cross-tabulations, and Cramer’s V.

	Cluster 1		Cluster 2		Cluster 3		Cluster 4		Cluster 5
	Experiential Golfers		Wellness-Oriented Golfers		Multifunctional Golfers		Low Involvement Golfers		Learning-Oriented Golfers
Items	N = 76 (19.95%)		N = 91 (23.88%)		N = 82 (21.52%)		N = 60 (15.75%)		N = 72 (18.9%)
	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
(1) Business opportunity	1.68	0.811	1.81	0.91922	5.03	0.93139	1.53	0.79616	2.68	1.158
(2) Economic benefit	5.87	0.9443	5.01	1.16955	5.52	0.92985	2.91	1.46506	3.74	1.17876
(3) Learning/challenge	5.77	0.7484	3.51	0.78778	5.51	1.02666	3.64	0.92525	5.29	0.86364
(4) Escape/relaxation	5.77	0.9816	5.32	1.12586	5.57	1.13224	2.75	0.99078	4.21	1.2652
Gender (V = 0.150; p = 0.028) [n; %]
Male	51	17.10%	67	22.40%	74	24.70%	50	16.70%	57	19.10%
Female	24	30.00%	24	30.00%	8	10.00%	10	12.50%	14	17.50%
Non-binary	1	50.00%	0	0.00%	0	0.00%	0	0.00%	1	50.00%
Age (V = 0.159; p = 0.04) [n; %]
Under 30 years	2	14.30%	1	7.10%	7	50.00%	1	7.10%	3	21.40%
Between 30 and 45 years	9	23.70%	5	13.20%	13	34.20%	2	5.30%	9	23.70%
Between 45 and 60 years	31	20.50%	37	24.50%	39	25.80%	24	15.90%	20	13.20%
Over 60 years	34	19.10%	48	27.00%	23	12.90%	33	18.50%	40	22.50%
Education (V = 0.109; p= 325) [n; %]
Primary education	2	28.60%	1	14.30%	1	14.30%	1	14.30%	2	28.60%
Secondary education	3	9.40%	12	37.50%	7	21.90%	2	6.30%	8	25.00%
Vocational training	18	31.00%	13	22.40%	13	22.40%	9	15.50%	5	8.60%
Postgraduate	12	14.60%	21	25.60%	21	25.60%	13	15.90%	15	18.30%
University degree	41	20.30%	44	21.80%	40	19.80%	35	17.30%	42	20.80%
Occupation (V = 0.158; p= 0.035) [n; %]
Public employee	12	27.90%	9	20.90%	7	16.30%	11	25.60%	4	9.30%
Unemployed	1	20.00%	2	40.00%	1	20.00%	1	20.00%	0	0.00%
Student	1	25.00%	0	0.00%	1	25.00%	0	0.00%	2	50.00%
Retired	24	18.60%	40	31.00%	13	10.10%	19	14.70%	33	25.60%
Household work	1	25.00%	1	25.00%	1	25.00%	0	0.00%	1	25.00%
Freelance worker	10	18.20%	8	14.50%	16	29.10%	9	16.40%	12	21.80%
Self-employed professional	6	17.60%	7	20.60%	10	29.40%	7	20.60%	4	11.80%
Private company employee	21	19.60%	24	22.40%	33	30.80%	13	12.10%	16	15.00%
Income (V = 0.118; p = 0.379) [n; %]
Less than 700 euros	2	50.00%	0	0.00%	1	25.00%	0	0.00%	1	25.00%
Between 700 and 1000 euros	0	0.00%	0	0.00%	1	100.00%	0	0.00%	0	0.00%
Between 1001 and 1500 euros	5	26.30%	1	5.30%	7	36.80%	4	21.10%	2	10.50%
Between 1501 and 2500 euros	21	22.60%	20	21.50%	23	24.70%	11	11.80%	18	19.40%
Between 2501 and 3500 euros	16	15.10%	32	30.20%	17	16.00%	17	16.00%	24	22.60%
More than 3500 euros	32	20.30%	38	24.10%	33	20.90%	28	17.70%	27	17.10%

shows the results obtained from the cluster analysis, the mean, standard deviation and z-scores for each of the variables and clusters, as well as Cramer’s V for each of the demographic variables.

The results in Table 10 show that the only variables with a significant Cramer’s V (p < 0.05) were age, gender and occupation. All of these had a weak effect on the clusters: V_Age = 0.159, p = 0.04; V_Gender = 0.150, p = 0.028; V_Occupation = 0.158, p = 0.035.

4.5.1. Cluster Interpretation

Regarding the motivational and demographic characteristics of each group, the results are shown in the

Table 11. Analysis Cluster. Demographic profile and motivations.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
	Experiential	Escape Golfers	Multifunctional	Low-Involvement Golfers	Learning-Oriented
	N = 76 (19.95%)	N = 91 (23.88%)	N = 82 (21.52%)	N = 60 (15.75%)	N = 72 (18.9%)
(1) Business opportunity	1.68	1.81	5.03	1.53	2.68
(2) Economic benefit	5.87	5.01	5.52	2.91	3.74
(3) Learning/challenge	5.77	3.51	5.51	3.64	5.29
(4) Escape/relaxation	5.77	5.32	5.57	2.75	4.21
Gender	Male	Male	Male	Male	Male
Age	Over 60 years	Over 60 years	Between 45 and 60 years	Over 60 years	Over 60 years
Occupation	Retired	Retired	Private company employee	Retired	Retired
Income	More than 3500 euros	More than 3500 euros	More than 3500 euros	More than 3500 euros	More than 3500 euros
Education	University degree	University degree	University degree	University degree	University degree

.

Cluster 1, known as ‘experiential golfers’, scores low on the ‘Business opportunity’ variable and high on all others. This indicates that they are not interested in golf trips for business purposes or to improve client relationships; their main motivations are to play golf more cheaply than in their local area, as well as to improve their skills and escape the crowds. They have been termed “experiential” because they place great value on the experience whilst also attaching significant importance to price; for these customers, the perceived value of the destinations they visit is very important.

Cluster 2, known as ‘escape golfers’, scores low on the ‘business opportunity’ variable; consequently, like the ‘experiential’ group, they do not use golf tourism trips for business purposes. Unlike the first group, they attach relative importance to the “learning/challenge” variable, with their main motivations being “financial benefit” and “escape/relaxation”. This group of golf tourists seeks an experience that allows them to disconnect from their daily routine, seeking escape and relaxation whilst also prioritising price. In the literature on segmentation based on motivations in active sports tourism, this group is comparable to that identified in the work by Hodeck and Hovemann (2018) and referred to as active and recreational tourists. In the literature on golf tourism, this group is comparable to that identified by Gibson and Pennington-Gray (2005), referred to as resort tourists, who prefer to engage in other types of activities such as going to the beach or enjoying cultural and sporting events.

The third group comprises tourists referred to as ‘multifunctional’. This group scores highly on all motivational variables and is consistent with other studies on golf tourism (J. H. Kim & Ritchie, 2012). Meanwhile, cluster 4, termed “low-involvement golfers”, scores low on all motivational variables; this may be because, despite being considered golf tourists, they may have other motivations not examined in this study.

Finally, cluster 5, “Learning-oriented”, scores low on “business opportunity” and medium to medium-high on the variables “economic benefit” and “escape and relaxation”; the main motivation is the “learning/challenge” dimension. This group of golf tourists seeks to improve their golf skills and knowledge, prioritising this over price or the desire to unwind.

4.5.2. Demographic Profile of the Clusters

Regarding the demographic profile of the different groups analysed, two distinct demographic profiles can be observed: the first comprises the “experiential golfers”, “escape golfers”, “low-involvement golfers” and “learning-oriented golfers”. This demographic profile is characterised by being predominantly retired men over 60 years of age with a university education and a high income of over €3500 per month. The second resulting demographic profile comprises the group of “multifunctional golfers”, characterised by being mostly men aged between 45 and 60, private-sector employees with a university education and high-income levels (over €3500). These profiles do not align in terms of age with other studies on golf tourism, in which the typical tourist profile is younger (J. H. Kim & Ritchie, 2012; Ramírez-Hurtado & Berbel-Pineda, 2015; S. S. Kim et al., 2008). This may be due to the specific characteristics of the Spanish golf tourism market, although further in-depth study is required to reach a reliable conclusion explaining this.

4.5.3. Cluster Preferences Analysis

The analysis of golf tourists’ preferences is shown in

Table 12. Analysis of Preferences of Golf Tourist Clusters.

	Cluster 1		Cluster 2		Cluster 3		Cluster 4		Cluster 5
	Experiential Golfers		Escape Golfers		Multifunctional Golfers		Low-Involvement Golfers		Learning-Oriented Golfers		Total
	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.
Preferences regarding place of posting
Climate	5.61	1.28	5.22	1.56	5.78	1.20	4.95	1.72	5.03	1.68	5.34	1.51
Accessibility	5.11	1.41	4.84	1.73	5.48	1.48	4.38	1.68	4.69	1.42	4.93	1.59
Safety and security	5.29	1.48	4.90	1.75	5.30	1.58	4.13	1.87	4.68	1.70	4.90	1.72
Prior knowledge of the area	3.93	1.75	3.71	1.67	4.67	1.70	3.00	1.67	3.63	1.66	3.83	1.76
Variety of courses in the area	5.41	1.59	4.77	1.56	5.60	1.46	4.25	1.76	5.10	1.48	5.06	1.62
Availability of other leisure activities	5.05	1.74	4.55	1.92	5.05	1.60	3.68	1.79	4.79	1.74	4.67	1.82
The area’s fame and reputation	5.08	1.40	4.34	1.54	5.28	1.30	4.25	1.60	4.78	1.44	4.76	1.50
Hotels and restaurants in the area	5.78	1.24	5.22	1.58	5.90	1.14	4.65	1.76	5.46	1.35	5.43	1.47
Proximity to my place of residence	4.25	1.66	3.95	1.88	4.23	1.81	3.63	1.72	3.49	1.82	3.93	1.80
Golf course preferences
Service received	5.96	1.17	5.82	1.16	6.33	0.85	5.43	1.27	5.72	1.15	5.88	1.15
Field quality	6.42	1.01	6.01	1.02	6.49	0.76	6.07	1.13	6.04	1.11	6.21	1.02
Restaurant	5.17	1.54	4.35	1.64	5.26	1.34	4.17	1.79	4.65	1.35	4.74	1.58
Green fee prices	6.45	1.19	6.24	0.97	6.21	1.19	5.38	1.51	5.50	1.28	6.00	1.28
Practical training facilities	5.11	1.50	4.26	1.71	5.38	1.27	3.62	1.50	4.56	1.49	4.62	1.62
Field management	5.49	1.43	4.74	1.57	5.78	1.11	4.10	1.58	4.94	1.45	5.05	1.53
Accommodation	5.67	1.42	4.70	1.79	5.76	1.29	4.42	1.75	5.22	1.50	5.18	1.64
Accessibility	5.26	1.44	4.75	1.66	5.61	1.26	4.28	1.74	4.85	1.62	4.98	1.60

. Among the preferences relating to the destination, the presence of restaurants and hotels in the area represents the construct with the highest score among golf tourists. This is consistent with other studies on sports tourism (Perić et al., 2019) in which the local culinary offering becomes an incentive and an opportunity for golf course managers to reach agreements with local businesses and organize events that combine the game with local cuisine in order to capitalise on these preferences.

The climate is the second most highly rated factor, scoring above 5 across all segments, as tourists leave their home countries in search of a favorable climate where they can play golf for most of the year. In the case of golf tourism in Spain, this is a constant factor for tourists from Nordic countries, who take advantage of the country’s good climate (Real Federación Española de Golf, 2024). Similarly, the possibility of playing golf for most of the year helps to deseasonalise tourism by balancing the arrival of tourist crowds (Babinger, 2012; Garau-Vadell & de Borja-Solé, 2008).

Accessibility, understood as the ease of travelling to the destination, is highly valued by experiential and multifunctional groups of golfers; in this regard, tourism managers should focus their marketing efforts on these tourist segments. This same group of tourists places greater importance on the safety and security of the location, which is also the fourth highest-scoring construct for the group. A safe, attractive and easily accessible destination is an important attribute for both active tourists and event organisers (Perić et al., 2019).

The quality of the course, the price of green fees and the service received are the pull motivational variables of golf courses most valued by golf tourists. This indicates that, in addition to good value for money, course managers must pay special attention to staff training and professionalism to secure customer loyalty.

The group of low-involvement, learning-oriented golfers do not place as much importance on green fee prices, whereas they do place importance on the quality of the course. This group of tourists are willing to pay a little more provided the course is of high quality and in optimal condition, without placing as much importance on special offers. Course managers can capitalise on this by tailoring their offers during the high season when prices are higher.

For their part, multifunctional and experiential golfers place a high value on the availability of restaurants and accommodation on-site, being the main consumers of such services. Golf courses offering these complementary services should target their marketing campaigns at this type of customer through promotional channels typical of hotels and present these features as a distinguishing factor from the competition. Furthermore, this group of tourists places greater importance than other segments on proximity to their place of residence, so marketing campaigns should focus on the local population and surrounding areas. Multifunctional golfers also place a higher value on the presence of several courses in the area, so this can become a differentiating factor for this sector of tourists.

Course management refers to the work of the marshal responsible for the flow of play, aiming to minimise waiting times for players as much as possible. This factor is highly valued by experiential and multi-purpose golfers who seek a destination with good weather and aim to make the most of their time to enjoy other local amenities, such as the local cuisine. Similarly, accessibility to the golf course—referring to the ease of access to the facilities—is of paramount importance to these tourist segments.

5. Discussion

This study helps to better understand the characteristics of golf tourists based on their motivations, preferences and demographic variables. Motivations help to understand and predict tourists’ behavior so that institutions and companies managing golf courses, as well as adjacent sectors, can develop specialised marketing strategies for each group of tourists based on their profitability (Crompton, 1979; Visintin et al., 2026).

The analysis, carried out using exploratory factor analysis, identified four main factors or motivations driving golf tourists: business opportunities, financial savings, learning and challenge, and escape and relaxation. The motivations of escape and relaxation, and learning and challenge, are consistent with studies on tourism motivations (Crompton, 1979; Dann, 1977) and studies on sports tourism (Bason, 2023; Bichler & Pikkemaat, 2021; Carvache-Franco et al., 2025; J. H. Kim & Ritchie, 2012). Furthermore, business opportunity motivation also appears in other studies on golf tourism referenced here, yielding equally significant results (Barros et al., 2010; J. H. Kim & Ritchie, 2012).

In their study, Correia et al. (2009) distinguish between three types of motivation: social, sporting and leisure related. In the present study, the motivational constructs identified are similar, except for the social factor, which did not yield satisfactory results in the AFE due to failing to meet the minimum number of variables, despite achieving high scores. This may be due to various factors, such as the characteristics of the sample collected, the definition of the factor, or a possible shift in the motivations of golf tourists over time. In any case, further research would be necessary to corroborate the results of this study and verify whether a change in the motivations of golf tourists is taking place.

The motivation to save money is one that has not previously been studied in the context of golf tourism, despite the importance of financial constraints in sports tourism Gibson (1998). Although this constraint has not been analysed as a motivation for taking a golf trip, it has been considered, for example, in the work of Correia et al. (2009), who used the economic factor as an expectation in their analysis to identify different segments of golf tourists visiting the Algarve. J. H. Kim and Ritchie (2012) distinguish in their study the ‘benefits’ motivation, which includes motivations such as ‘I can avoid bad weather’ or ‘I can combine different activities during my golf holidays’, which have been excluded from the present study as they did not yield satisfactory factor loadings. Thus, in the present study, the ‘cost-saving’ motivation refers solely to aspects related to the price of playing golf, such as ‘I can play more rounds of golf more cheaply’, ‘I can play without needing a membership’ and ‘I can travel at a lower cost than playing golf domestically’. This suggests that golf tourists attach considerable importance to price and to the need not to have to spend so much money to enjoy this sport. However, although it has not been used as a motivational factor in previous studies, tourist expenditure has been considered as a segmentation element in earlier works on golf tourism (Barros et al., 2010).

Furthermore, in their work J. H. Kim and Ritchie (2012) include a fifth factor or motivation: social interaction and kinship. Although this motivation has been identified as one of the main motivations for undertaking a trip (Crompton, 1979; Dann, 1977), in this study, no factor loadings were found to be sufficiently high across a minimum number of variables to be taken into account. This may be due to the specific characteristics of the sample used or to a shift in the motivations of active golf tourists; however, further investigation in future studies and a comparison of the results would be necessary to better understand this outcome.

Following the exploratory factor analysis, a confirmatory factor analysis was conducted to verify the robustness of the latent constructs identified. The results obtained are satisfactory; only one of the indicators did not achieve an acceptable score (RMSEA < 0.08) (Hu & Bentler, 1999; Romero & Mora, 2020), which represents a limitation in the model’s fit. Nevertheless, this is offset by the positive results of the other fit indices (chi-square, TLI, SRMR, GFI, RNI, IFI, GFI and NNFI), so it can be concluded with little margin for error that the model fits satisfactorily.

Finally, the cluster analysis has resulted in the classification of golf tourists into five clusters. The first of these, termed ‘experiential tourists’, rate the ‘business opportunity’ motivation significantly lower than the other motivations, whilst giving high scores to the remaining motivations. This group can be compared to that identified by J. H. Kim and Ritchie (2012), which they term ‘intensive golfers. This group of tourists does not seek to use golf as a source of income or to leverage it for client networking, but rather seeks to improve their game, escape the crowds and receive superior perceived value without losing sight of the price.

The second group, termed “escape golfers”, can be compared to the cluster identified in the work J. H. Kim and Ritchie (2012) “golf companions”. They do not score particularly highly on the motivations “business opportunity” and “learning and challenge”, placing greater importance—especially according to the results of this study—on using golf holidays to escape the daily grind and the crowds, whilst still attaching relatively high importance to the price they pay for them. This type of tourist can be compared to the group referred to as ‘resort tourists’ in the work by Gibson and Pennington-Gray (2005) on active golf tourists, who place greater importance on the availability of other activities such as the beach or attending cultural and sporting events. Studies on active sports tourism also show similarities with the so-called ‘active and recreational tourists’ group identified in the work by Hodeck and Hovemann (2018). In practical terms, it can be said that these are tourists who regard participating in sport as a secondary priority and like to complement their holidays with additional activities alongside sport.

Cluster 3, termed ‘multifunctional golfers’, scores highly across all motivations (business opportunities, financial gain, escape and relaxation, and learning and challenge). In the golf tourism literature, this is comparable to the group of multi-motivated golfers described in (J. H. Kim & Ritchie, 2012). Similarly, in the active sports tourism literature, they can be compared to the group of tourists who want it all, as described in Hodeck and Hovemann (2018).

The last two clusters are presented as novel in the literature on golf tourism. The first of these is termed “low-involvement golfers” and they give medium-to-low scores to all motivations, with learning and challenge being slightly higher than the rest. Although not identical, this group does bear a resemblance to the group of tourists identified by Correia et al. (2009), which places greater emphasis on other alternative activities than on playing golf. Finally, the last group analysed is that of “learning-oriented golfers”, which shows average scores for business opportunity and financial gain, and high scores for learning and challenge, as well as escape and relaxation. This group of tourists is characterised by having no intention of doing business whilst on their golf holidays; rather, they are focused on improving their skills and disconnecting from their routine without placing much importance on the price they must pay for it. This group can be classified within the category identified by Terzić et al. (2021) in the context of active sports tourism as ‘competitive’; according to Hungenberg et al. (2016), Petrick et al. (2001) and J. H. Kim and Ritchie (2012), such travelers seek competition and challenges when engaging in tourism and sport.

As for the demographic characteristics of the different clusters, two distinct demographic profiles can be observed: on the one hand, those belonging to the ‘experiential golfers’, ‘escape golfers’, ‘low-involvement golfers’ and ‘learning-oriented golfers’ groups, who are highly likely to be men over 60, retired, with a university education and a high income of over €3500 per month. On the other hand, the demographic profile of the cluster known as “multifunctional golfers” is predominantly a man aged between 45 and 60, with a university education, employed in the private sector and with an income exceeding €3500 per month. These results are consistent with previous research on golf tourism (Gibson & Pennington-Gray, 2005; J. H. Kim & Ritchie, 2012; Ramírez-Hurtado & Berbel-Pineda, 2015; S. S. Kim et al., 2008).

6. Conclusions

This article highlights the importance of golf tourism as a sub-sector of sports tourism and the scarcity of studies analysing the motivations of active golf tourists on their trips to better understand their behaviour. Furthermore, through segmentation, it has been demonstrated that active golf tourists in Spain can be grouped into homogeneous and heterogeneous clusters, thereby providing a better understanding of their characteristics and suggesting practical applications so that golf course managers and tourism managers in general can develop specific strategies in terms of pricing, positioning and promotion.

Through this study, the researchers aim to contribute to the scientific literature on the study of the motivations of active sports tourists by providing a motivational framework that can subsequently be generalised to other regions of the world or even to the analysis of the motivations of active tourists in other sports. Furthermore, the aim is to fill a gap in the literature on the study of the motivations of active golf tourists in Spain, given the lack of studies that utilise motivation theory for the segmentation of active sports tourists in this region.

With regard to the results, through the analysis of the motivations of golf tourists in Spain and by means of exploratory and confirmatory factor analysis, four main motivations have been identified (business opportunities, financial savings, learning and challenge, and escape and relaxation), which have enabled the segmentation of golf tourists in Spain into homogeneous groups. A K-means cluster analysis has identified a total of five clusters with distinct motivational characteristics, namely: experiential tourists, wellness-oriented tourists, multifunctional tourists, low-involvement tourists and learning-oriented tourists. In addition, two demographic profiles were found to be common across all clusters: on the one hand, a 60-year-old retired man with a university education and an income exceeding €3500 per month; and, on the other hand, a man aged between 45 and 60 working in the private sector, with a university education and an income exceeding €3500.

In addition, the preferences of golf tourists regarding the destination and the golf course were analysed, with the climate, accessibility and the availability of restaurants and hotels in the area standing out in the first category. For their part, golf tourists placed greater importance on the quality of the course, the price of green fees and the service received as the most highly valued qualities of the golf course.

The results of this study are of great interest to companies in the golf sector, enabling them to develop specific marketing strategies. Furthermore, it contributes to the scientific literature on the study of the motivations of active sports tourists by providing a motivational framework, and more specifically to the study of the motivations of active golf tourists, given the scarcity of research in this area.

6.1. Practical Applications

The practical applications derived from the findings of this study into the motivations of golf tourists in Spain are aimed both at public institutions responsible for promoting golf tourism and at managers of golf courses and related sectors. Depending on the different profiles of golf tourists identified, specific practical applications can be developed for each one. This section outlines some possible practical applications based on the scores for the various motivations; in this way, golf course managers will be able to tailor their approach depending on the type of tourist they wish to target.

The experiential golfers segment scores above average on the motivational constructs of ‘escape and relaxation’, ‘cost savings’ and ‘learning and challenge’, whilst also placing high importance on destination factors relating to climate, the availability of local amenities, and the safety and security of the area. As for their preferences regarding golf courses, they highlight the price of green fees, the quality of the course and the service received. Taking these characteristics into account, golf course managers who can offer a variety of activities, such as other leisure or cultural activities, and who have restaurants in the area should focus their efforts on trying to attract this type of golfer. An interesting approach would be to secure agreements with businesses in adjacent sectors, such as the hospitality industry or those related to culture, to meet the needs of this segment. Furthermore, they are heavily influenced by the safety and security of the area; therefore, regions with issues of terrorism or territorial instability are likely to be quickly ruled out by this group of golf tourists. The price of green fees and the quality of the course are also major factors, so this segment would be of interest for complementing the low season in Spanish golf, which coincides with the summer months, whilst also taking advantage of the high season in other sectors such as the hospitality industry. This tourist segment would be of interest not only to courses in high-traffic areas such as the Costa del Sol or the Canary Islands, but could also be utilised by managers in other regions with lower visitor numbers, capitalising on the excellent cuisine found throughout the country and the opportunity to play golf more cheaply on high-quality, albeit less famous, courses.

The tourist segment known as ‘wellness-oriented tourists’ places particular emphasis on the motivations of ‘escape and relaxation’ and ‘cost savings. For this group of golfers, the sport itself becomes secondary, with particular importance being placed on the availability of restaurants in the area and the climate. Regions that combine these two factors should direct their marketing efforts towards this group of tourists. Likewise, golf course managers should consider incorporating gastronomic alternatives into their offering, such as introducing tastings of local food or securing agreements with local hospitality businesses through discounts, thereby creating a highly profitable and attractive synergy for this group of tourists. The importance of green fee prices is also significant, so managers should strive to attract this type of tourist during the low season by promoting special offers at reduced rates.

So-called “multi-purpose golfers” are the most profitable segment of tourists for golf courses, and it is on them that marketing efforts should be focused during the high season. This group of golfers attaches great importance to the price of green fees; however, for them, the quality of the course and the service received are paramount, meaning they are tourists seeking a premium experience who value the experience itself more than the price they pay for it. Furthermore, this is the group that places the greatest importance on the “business opportunity” motivation; consequently, using golf as a tool to improve client relations or seek business opportunities is typical of this type of tourist. Primarily, these are private sector businesspeople, so establishing partnerships with private companies that can help attract other members may be a way to attract this type of client. Attracting them through business events can also be an effective way to draw them in. Course management regarding the time taken per round, which is influenced by waiting times, is very important for this segment; therefore, managers targeting this type of customer must be very careful with tee times and any potential bottlenecks that may arise. The range of hotels and restaurants in the area and the variety of courses are highly valued factors; therefore, organising multi-day events involving several courses, as well as agreements with well-regarded restaurants and hotels, can set you apart from other tourism managers.

Low-involvement golfers do not score highly on any motivational construct, which can serve as a wild card for golf managers, as they could adapt to any strategy managers establish for the other profiles. They attach relatively high importance to accessibility, as well as the presence of restaurants and hotels in the area and the climate; consequently, this segment will be more inclined to visit destinations with good transport links to their home town, rather than those with poorer connections, even if the latter offer better facilities and a more comprehensive range of tourist and golfing options. The availability of accommodation is a key factor for this type of tourist, so golf courses offering on-site accommodation will be particularly attractive to this segment.

Finally, learning-oriented golfers can be compared to the segment of tourists analysed in the work by Gibson and Pennington-Gray (2005), referred to as ‘pure golf tourists’. This segment is characterised by the fact that the primary reason for the trip is to play golf, with all other factors taking a back seat. They are interested in playing on various golf courses and place a very high value on the quality of the course, regardless of the price they pay for a round. This type of golfer is the one that golf tourism managers must focus on most, as they are the driving force during the high season. In areas where there are several golf courses in the same vicinity, golf course managers must focus on offering them an added bonus to ensure their loyalty to their facilities. Furthermore, this group of golfers tends to be very profitable for businesses complementary to the golf course, such as the restaurant or the shop, as they usually purchase items from the course as souvenirs.

Securing agreements with companies in adjacent sectors, as well as with other golf courses in the area, can create a significant competitive advantage for tourist destinations, setting them apart from the rest. Likewise, incorporating other complementary activities such as outdoor sports, cultural events or promoting the local culture through guided tours can prove highly attractive to golfers in general, helping businesses to stand out from the rest. Another key factor for tourists is the service they receive, making the training and professional development of golf club staff essential for attracting and retaining golf tourists. Technology and facilities that support work–life balance for golf tourists, such as the provision of childcare, are also of great importance to this sector.

6.2. Limitations

Among the limitations of this study is the inability to generalise the results obtained in terms of practical applications for other regions of the world, as the data were obtained through convenience sampling rather than random sampling, which limits their implications to businesses in the sector in Spain.

Furthermore, the exclusion of the ‘social interaction and kinship’ factor is due to its failure to meet the minimum number of variables per factor, despite having high factor loadings. This means that the variables in this subgroup were indeed important but have been excluded by the AFE. For future research, it is recommended that more variables be added to this sub-segment so that they meet the minimum number of variables per factor and can thus be considered in the analysis.

This study has not explored in depth the role that sport plays within the trip, that is, whether it is the main motivation for the trip or a complementary activity; it is therefore recommended that future research be conducted in which respondents specify the role that golf plays in their journey.

6.3. Future Research Lines

As future lines of research, it is proposed to carry out further statistical analyses using different tools in order to compare the results, such as discriminant analysis or neural network analysis. Furthermore, it is recommended to conduct studies that increase the sample size and use data collection techniques that can be extrapolated to other regions through random sampling. Conducting studies that analyse motivations in other countries is also of great interest to the literature, as it would allow the results obtained to be compared with those of the present study.