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Characterization of Profiles as Management Strategies Based on the Importance and Valuation That Users Give to the Elements of the Golf Courses

Faculty of Educational Sciences and Sports, University of Vigo, 36005 Pontevedra, Spain
Faculty of Sport Sciences and Physical Education, University of Coruña, 15179 Oleiros, Spain
Faculty of Psychology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
Author to whom correspondence should be addressed.
Sustainability 2020, 12(11), 4781;
Received: 16 May 2020 / Revised: 5 June 2020 / Accepted: 9 June 2020 / Published: 11 June 2020
(This article belongs to the Special Issue Strategic Planning of Sports Systems)


The main objective of this study was to characterize golfers’ profiles based on the importance and valuation that users give to the elements of golf courses, considering the segmentation as key to making strategic decisions. This was done using a priori segmentation of the traditional style against a post hoc segmentation based on the importance that users concede to variability of elements of golf courses. Moreover, the relationship between the quality attributed to the service dimensions and the golf course global performance was examined for each of the obtained segments. The results have established certain specific characteristics of the typical golf user and two large segments of golfers were identified from Cluster Analysis (Competitives and Socials), they were made up of the following subgroups: Regular-leisure and business and matures-familiar. The causal analysis of covariance structures for each segment revealed partially different interests. This study provides useful information for managers to design management strategies that address the different characteristics, interests, and priorities of their customers and potential customers in order to win them, keep them, satisfy them, and build their loyalty, ensuring the development and sustainability of the entity.

1. Introduction

Nowadays, understanding consumer behavior and the identification of the main features of a product and services, which play an important role in the consumer’s decision-making, constitute the main concern of the marketing programs of any organization [1,2].
Market segmentation is a key tool in marketing management [3]. According to Miquel et al. [4] (p.30), segmentation is differentiation of the total market of a product or service, in a certain number of elements, people, or organizations, homogeneous among them and different from others in terms of habits, needs, and tastes of their components, which are called segments, obtained by different statistical procedures, in order to apply to each segment the most appropriate marketing strategies to achieve the objectives established a priori by the company. To assume that the market is not uniform and that there are different groups of consumers with different profiles and particular needs, attitudes, motivations, experiences, and behaviors, enabling organizations to better understand the markets in which they are inserted and be more competitive.
In practice, diverse strategies and procedures have been proposed to try to segment a market [5]. Socio-demographic criteria have been used to categorize subjects depending on age, gender, occupation, professional status, level of education, or income. These criteria have been shown to have a high explanatory capacity, but they have been losing importance in mature markets.
Different authors [6,7,8] have noted the increasing relevance of subjective variables, such as lifestyle, motivations, attitudes, preferences, or importance [9,10], as referents more powerful in characterizing of consumers.
Nowadays, it is common to opt for strategies capable of integrating both types of information, through the use of advanced multivariate methods, such as Cluster Analysis [11] or the CHAID procedure [12,13].
In the case of sports services of which nature is intangible, the market segmentation is essential [14,15,16]. Several studies [17,18,19] suggest that the way to achieve the satisfaction of users of sports services may be different depending on the segment or profile in question.
The golf industry has not escaped the need for a detailed analysis of user´s profile, which, in recent years, has gone from being an expensive game of riches to a mass hobby [20,21]. This assessment seems essential when its social projection has achieved in recent years more than 60 million players across the world [22] and it is currently considered as the primary sport in the world in terms of financial expenses [23,24].
These figures allow describing it as a massive sport, and every year, more women and children begin to play golf, to emphasize the social importance of this activity. In addition, golf constitutes a business area of rising importance, not only linked to touristic services, but also associated with the amount of employment which this activity generates in different organizations [22,25].
Golf Around the World [26] shows more than one-third (36%) of the U.S. population played, watched, or read about golf in 2018. Europe has the second largest regional share with 23% of the world’s total. England is the number one golfing country in Europe with 2270 golf courses and 31,620 golf holes. Spain takes sixth place in Europe with 497 golf courses, 7071 holes, 471 facilities and 269.853 federative licenses (71.73% males/28.26% females), of which 10.452 are licenses from Galicia (72.4% males/27.6% females) [27].
As a result, the typology of golfers has been progressively diversifying from socio-demographic, attitudinal, and motivational the point of view. Besides, the concept of golf club itself has evolved into something complex and multidimensional, which has attracted the interest of recent studies.
Most of these studies focus on the segmentation of golf market as promotion strategy, linking the golfers’ perception of quality of service with satisfaction, the perceived value and other different variables of interest, such as gender [28,29], age [30], the player´s limitations and experience, motivation [31], participation [32], or exercise adherence intention [21]. However, most related publications are focused on the perspective of golf tourist and intention to return to destination [33,34,35], rather than making a comprehensive segmentation of golfers in a community or in a particular country. In the latter case, it would be possible to identify and characterize the different existing profiles and typologies, also considering the different types of golf courses and management methods as a form of strategy that allows the improvement and sustainability of these sports clubs.
In this sense, sustainability must be an element in the long-term planning of golf organizations, from a social, economic, and environmental point of view.
For all the above, the purpose of this study will be to characterize golfers´ profiles based on the importance and valuation that users give to the elements of golf courses, considering the segmentation as key to making strategic decisions.
To identify the existence of different users´ profiles, two complementary strategies will be used. On the one hand, a priori segmentation of the traditional style based on socio-demographic data, motivational, sports practice aspects, and habits linked to playing golf. On the other hand, a post hoc segmentation to identify profile subtypes from Cluster Analysis using different stages.
In addition, the relationship between the quality attributed to the dimensions referred to service and the golf course global performance for each of the main segments previously obtained from Cluster Analysis will be examined. In this way, an attempt will be made to verify to what extent the ways to achieve the approval of golf users may vary depending on the type of golfer, since if so, it will be necessary to implement different recruitment and/or loyalty strategies that allow the sustainability of these sports entities, through the continuous adaptation of the service to such a changing society and market.

2. Materials and Methods

2.1. Participants

Participants were 968 golf users of Galicia—Northwest of Spain—belonging to 13 different clubs with different types of installations (golf courses of 9 and 18 holes, and pitch and putt) and management forms (private, public, commercial, and mixed). Of these, 796 were men (82.2%) and 172 women (17.8%). Their ages were between 16 and 79 years old (x = 47.58; Sx = 12.51), more than half (57.75%) between 41 and 60. Most of them were club members (59.7%) and had a moderate level of golf with handicaps between 11.6 and 18.4 (Table 1).

2.2. Process

The selective methodology was used. Data were collected from a closed structured interview conducted by interviewers unconnected with the club and trained specifically for this (supervised by the person responsible for the study and authorized by the manager). To collect information, interviewers used an ad hoc questionnaire. Participants were selected accidentally, voluntarily and, following the advice of golf course managers, during the busiest months of the year (spring and summer seasons). In addition, to try to make up for the lack of randomness in the selection of the sample units, the data from each club were collected on different days and time zones, to try to achieve the highest possible degree of sample representativeness. The interviews had an approximate duration of 15 min.
Participation in the study was completely voluntary, making special emphasis on the guarantee of confidentiality and anonymity referred to the answers. Written informed consent was obtained from all individual participants included in the study. The research protocol followed the principles of the Declaration of Helsinki regarding research involving human subjects—64th World Medical Assembly 2013. The study was approved by the management board of the golf courses, as well as by the Ethics local Committee.

2.3. Instrument

The ad hoc questionnaire used was structured and closed, composed of a section of segmentation where socio-demographic, motivational, attitudinal and sport practice information (Figure 1) appeared, and on the other hand (Figure 2), a section of assessment of the importance-valuation of elements that make a golf club, with a general assessment section that included three additional items as performance indicators: Global assessment, degree of fulfillment of expectations, and general user satisfaction. The scale used was based on previous studies [36,37], obtained from Confirmatory Factor Analysis (CFA) (Table 2), with high internal consistency both globally (αGlobal = 0.91) and for each of the dimensions (αPersonal = 0.87; αFacilities = 0.86; y αCourse/Play zone = 0.76 (α = 0.92) and high ability to explain user satisfaction (R2 = 0.724).

2.4. Analysis of Data

To identify the existence of different users’ profiles, two complementary strategies were used: (1) We proceeded to a descriptive and univariate analysis of the various questions of the questionnaire referred to socio-demographic, motivational, and sports practice aspects. (2) We carried out a two-stage Cluster Analysis [38,39] to identify profile subtypes performing a hierarchical Cluster Analysis in the first, exploratory stage, and an iterative Cluster Analysis in the second stage to obtain the final solution. On the hierarchical Cluster Analysis—cluster procedure of the IBM SPSS Statistics 24.0, Euclidian distances were chosen as the similarity measures and Ward’s algorithm was used as the most statistically powerful method compared to other agglomerative clustering techniques because of its use of F values to maximize differences among clusters [40,41]. The resulting dendrogram was examined, checking the distance of each subject to his/her merged cluster and the size of each cluster, finding the four-cluster solution as the optimal one. In the second stage of the two-stage clustering procedure, k-means clustering was conducted using the means of the four-cluster solution as the starting seed points. This k-means procedure was used to improve the initial assignment of subjects to clusters and obtain the final solution. Iterative procedures, such as this one, are more powerful and reliable than hierarchical ones but need previous specification of the number of clusters and its initial centers.
The resulting groups or subtypes were compared on socio-demographic, motivational, and behavioral variables not included in the original clustering process in order to validate and adequately characterize each of the clusters. These qualitative variables were analyzed with cross-tables using chi-square analyses to assess the global significance and corrected standardized residuals [42] to know the specific associated categories.
In addition, in order to examine the relationship between the quality attributed to the dimensions referred to service and the club’s global performance, for each of the segments obtained from Cluster Analysis. Structural analysis of covariance was carried out using the dimensions concerning the perceived quality as predictors and three performance indicators as criteria.

3. Results

3.1. Distribution of Practitioners According to Socio-Demographic Variables

Socio-demographic variables show that most are men (82.2%), the average age is 47.5 years old, 68.5% have a university education, and occupations are mostly entrepreneurs (17.9%) and civil servants (16.9%).

3.2. Distribution of Practitioners Based on Motivation and Sports Practice

The main reason (62.2%) for playing golf is its recreational character, followed by improving physical condition (12.2%) and competing (8.2%). The main barrier is the lack of time in general (44%), followed by work (28%) and climatology (11.7%). Sixty percent of golfers also practice other sports (tennis 11.5%, fitness 10.8%, paddle and football 10.7%, swimming 10.5%) and 51.7% do golf tourist travels.

3.3. Distribution of Practitioner According to the Type of Practice of Play

More than half of users (59.7%) are members of a club, have handicap between 11.6 and 26.4 (third category), play golf once or twice a week (87.5%), in the company of friends (66.7%), alone (11.2%), or in family (7.1%), for nearly four hours. The age at which they started is very variable, but on average it is 39 years old.
The most commonly used services in the club are essentially the practice course (58.4%), coffee shop (14.8%), the putting green (8.1%), and room of clubs (7.1%). In addition, 86.2% have used the facilities and courses of other golf clubs.

3.4. Importance of Different Elements of Golf Club

It is indicated below (Table 3) the importance that users give to each of the 25 elements of a golf club considered by Serrano-Gómez et al. [29]. Although all the elements obtained high averages, five of them stand out with averages higher than 4.50: Maintenance–course condition (4.70), cleaning-hygiene (4.65), treatment and care (4.58), professionalism of teachers (4.55) and physical state–maintenance of facilities (4.54).

3.5. Analysis of Simple Variance for the Comparison of the Four Groups

The results of the two-stage Cluster Analysis revealed four reliable subtypes that were differentiated by the importance they concede to various elements that shape the perception of a golf course, as well as their demographic, motivational, and usage profile (Table 4).

3.6. Contingency Tables Crossing the Cluster of Membership with Different Variables of Interest

The results of contingency tables crossing the cluster of membership with different variables of interest are shown in Table 5. The contrast value of independence χ2 and its statistical significance are collected.

3.7. Cluster Analysis

The results of the Cluster Analysis identify 4 subgroups of golf club users: (1) Regular, (2) leisure and business, (3) matures, (4) familiar. However, the proximity between the subgroups (Figure 3) makes the first two join in a conglomerate or cluster of second level (Competitives), while subgroups 3 and 4 make up second macro-cluster (Socials).
Although it is important to note that, in any case, profiles or types are pure, the analysis carried out allows to establish some characterization of each of them:

3.7.1. CLUSTER 1. Competitive Regular

The members of this subgroup (22.8%) give importance, especially to (1) the maintenance and course condition, (2) cleaning and hygiene of facilities, (3) the treatment and care of staff, and (4) the professionalism of management. Here, are mainly concentrated users of 18 holes golf courses, it is where there are more men and young people under 30 years, and a higher proportion of people with university education (64.2%). They started playing at an early age and have a relatively low handicap in comparison with other subgroups (they are better players). Professional occupations are diverse, although it is the subgroup where there is a greater number of senior managers. They play with friends almost every week, about three hours and a half, for fun and exercise. Lack of time and work reasons is the main barrier. They usually use the practice course and putting green. In all, Cluster 1 could be labeled as Competitive regulars, for whom golf is relatively serious and also a chance to play sports and keep fit.

3.7.2. CLUSTER 2. Competitive of Leisure and Business

This subgroup (7.2%), although it shares elements of the previous one, clearly gives less importance to all aspects of the service. It only grants importance higher than four to the golf teacher. Like the first cluster, they are mostly golf course users and less of pitch and putt. The age ranges from 45 to 60 years old, they have a handicap medium (slightly worse than subgroup 1) but better than socials. Comparatively, it is the cluster with less university education and where there is greater presence of civil servants and self-employed. Most are members and it is where they most use correspondence between clubs, maybe that is because there are more individuals playing alone. Like the other subgroups, most play weekly, although this is where there is a greater proportion of subjects that play occasionally. They do it mainly to have fun, although comparatively with the rest they indicate more—the opportunity to make contacts and business. On the other hand, they are the ones who participate more in tournaments, their main barrier is the lack of time and the work, adding also as an impediment “the distance to the club”. They like walking, cycling, swimming, and paddle. They are the ones who use more the practice course and less the coffee shop. Thus, Cluster 2 could be labeled as Competitives of leisure and business, for whom golf is an occasion to have fun, make contacts, and do some exercise, and although they are not expert golfers, they also like to take advantage of the hours they attend the club to improve their performance and be a little more competitive, so they concede special importance to golf teacher.

3.7.3. CLUSTER 3. Social Mature

It is the largest subgroup (36.7%). They give high importance to the different aspects of the club, but especially, the state of the course, cleaning, and hygiene, the treatment of staff and golf teacher. They are the ones who go more to pitch and putt, have an average age slightly higher than the rest (more than 50), and with a certain presence of retirees. They began to play later (over 40 years old), being their handicap is the highest, they are also the ones who play less frequently and although their motives of practice are varied, mainly they do it to have fun, to exercise, to enjoy the nature, and socialize, they are the least likely to do it to compete. The main barrier is the lack of time and the climatology (rainy). They play with friends and club mates and rarely alone. They also practice fitness regularly, they are the ones who use more the services of the club, particularly the practice course, but also the shop, rental equipment, and coffee shop. This subgroup is the one that travels less to golf tourist destinations. Although the cluster is more voluminous and heterogeneous, it could be labeled as Social Mature, in that they consider golf an occasion to meet friends, hang out, and do a little bit of everything.

3.7.4. CLUSTER 4. Social Family

With 33.3%, it is the one who gives more importance to all aspects of the club. It is the subgroup with more women, users of pitch and putt and where there are more practitioners of free use. It includes people of different ages, with medium-high handicap (fourth or fifth category) but somewhat lower than subgroup 3. They play mainly for fun and doing sport, but also to release stress and enjoy nature. They come with friends, family, and/or partners. They mostly allude to work and family reasons as barriers for not playing more frequently. They also practice other sports: Swimming, Pilates, and fitness. Comparatively, they are the ones who use less the practice course and more the restaurant and the coffee shop. For all, Cluster 4 could be labeled as Social Family, considering golf as an opportunity to relax, enjoy nature, and spend a different day with friends, family, or partners.

3.8. Causal Covariance Structure Analysis by Segments

Customer orientation, permanent improvement, and a decided effort towards quality have become the best path to achieve sustainability and survival for any sector.
In order to examine the relationship between the quality attributed to the dimensions referred to service and the club global performance, it was carried out a causal analysis of covariance structures, for each of the main segments obtained from Cluster Analysis: Competitives and Socials, using the dimensions concerning the perceived quality as predictors and three performance indicators as criteria—overall assessment, confirmation of expectations, overall satisfaction.
The final model obtained in the case of users of Segment I is shown in Figure 4, while the one obtained for users of Segment II is presented in Figure 5.
The explanatory capacity of the model is very similar in both cases—72% in Segment I and 74% in Segment II. Although, in both cases, the factor or dimension that has greatest weight is staff (γ = 0.71 and γ = 0.68, respectively), the second most important factor in the first case is the course (γ = 0.17), while in the second is the facilities in general (γ = 0.23).
For the first segment the factor of facilities would not have a significant weight (γ = 0.01; t = 0.29; p = 0.77), while for the second segment it would not be the course (γ = −0.08; t = −0.55; p = 0.58).
Furthermore, goodness-of-fit indices achieved are shown in Table 6. It should also be noted that the fit of the model in statistical terms is greater in the first than in the second segment, although this is a much more numerous and heterogeneous segment.
To a large extent, this confirms the results obtained in the cluster analysis, revealing partially different interests and priorities. While the users of Segment I, a priori more experienced and demanding, would be more sensitive to the technical characteristics of the course, the playing zone or the course, for users of Segment II, would be minor issues, being more important the social hall and the clubhouse, furniture and equipment, changing rooms, etc., possibly because recreational and/or social motivations prevail more in this segment.

4. Discussion

As for the initial segmentation, it is possible to emphasize with regard to gender and age, that the obtained results are similar to other studies, at national and international level. Most golfers are men [27,28] and they are, on average, around 48 years old [43]. Concerning golf tourists’ personal capabilities, López-Bonilla et al. [16] indicated that men reached a better handicap than women, but the latter have fewer years of experience than the former. As McGinnis et al. [44] (p. 4) recently pointed out, females do not have the requisite time to develop their golf skills. This coincides with the results obtained in Cluster 4, when women are the ones who most allude to work and family reasons as barriers to not playing more frequently.
In the same line as Paniza [45], the golf player usually has higher studies and qualified occupations, emphasizing as more frequent occupations the entrepreneur and civil servant. The main reasons for practice are to have fun and do exercise, and in general, the main barrier is the lack of time and work reasons. These results are similar to those obtained by Shim et al. [21], considering that motivation factors had a significant effect on exercise adherence intention: Health-orientation motivation, achievement motivation, and pleasure-orientation motivation.
It should be noted that most participants say that one of the most important aspects of golf is the ability to escape and relaxation for players.
Likewise, previous works [46] did not find significant motivational differences between men and women golfers, although in the sports repertoire and the barriers to play more, specifically, they indicate that the family responsibilities have a specific weight greater in women, a fact that suggests to managers the possibility of incorporating activities aimed at the youngest and families. In this line, Lee et al. [28] conclude that tangibles and empathy are dimensions of service quality that are fundamental to determining the satisfaction of male and female golfers, with women tending to pay more attention to physical representations, cleaning, and attention to the user.
With regard to golf practice, most users are club members, have a handicap between 11.6 and 26.4, and about 10 years of experience. These data coincide with other studies, such as that of Dewar and Kavussanu [43], where golfers have an average handicap of 13.37 and 12.23 years of experience.
The most used services are the practice course and the coffee shop. The latter, as noted by Weed and Bull [47], may be more interesting than even the play itself for a segment of players whom it calls associates, and that in this work could coincide with the Social segment. These findings and others related to attitudinal variables and sports practice are also in line with those found by Lee et al. [28] in private golf clubs, however, differ slightly from those obtained by Hennessey et al. [33] with golf tourists. All of this suggests that management strategies should be different depending on the type of entity (private, public, commercial, mixed), and therefore, the profile of the user.
In addition, the post hoc segmentation identified four possible profiles of golfers attending to the input importance, these were: Competitive regulars, Competitive of leisure and business, Social mature, and Social family. In this line, the majority of publications on the segmentation of golf users are developed in the field of tourism. However some similarities are observed between the results of this work and, for example, Hennessey et al. [33] who distinguish three clusters according to the frequency of play: Infrequent, moderate, and dedicated, Kim and Ritchie [34] depending on the reasons of the trip: Intensive, motivated by multiple reasons, and accompanying golfers, or Weed and Bull [47] that group them according to the rounds of play performed per year as participants (experts and beginners) and associates (avid, non-fans and nonparticipants).
Finally, we agree with Shim et al. [21] when they conclude that emotional satisfaction and performance satisfaction of golf range users had a significant impact on exercise adherence intention, what we believe can be translated into greater sports practice in golf clubs, loyalty of users, and sustainability of the entity.

5. Conclusions

In conclusion, the results obtained express the potential of the combination of both strategies (a priori and post hoc), making possible an integral segmentation of golfer in Galicia (Northwest Spain), identifying and characterizing possible different profiles or typologies.
This characterization and identification of profiles of the golf practitioner will be very useful for managers of these facilities, allowing them to design management strategies that meet the characteristics, interests, and priorities of their customers and potential customers (increase in the presence of women and children) in order to gain, maintain, satisfy, and keep them, guaranteeing with it the development and future of the entity.
Future works with different population samples and incorporation of new procedures will allow for the resolution of possible limitations and progress in the identification and characterization of current golf users, in order to develop better management strategies and continue to promote the practice of this discipline as sport beyond the tourist claim.

Author Contributions

V.S.-G., Ó.G.-G., V.G.i.P. and A.R.-B. contributed equally to this work. All authors wrote, reviewed and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Segmentation questionnaire for users of golf clubs (first part).
Figure 1. Segmentation questionnaire for users of golf clubs (first part).
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Figure 2. Questionnaire second part, level of global importance and valuation of each element, and overall valuation. Adapted from Serrano-Gómez et al. [29].
Figure 2. Questionnaire second part, level of global importance and valuation of each element, and overall valuation. Adapted from Serrano-Gómez et al. [29].
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Figure 3. Segments and subgroups obtained from Cluster Analysis.
Figure 3. Segments and subgroups obtained from Cluster Analysis.
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Figure 4. Standardized estimated parameters for the final causal covariance structures model (Segments I).
Figure 4. Standardized estimated parameters for the final causal covariance structures model (Segments I).
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Figure 5. Standardized estimated parameters for the final causal covariance structures model (Segments II).
Figure 5. Standardized estimated parameters for the final causal covariance structures model (Segments II).
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Table 1. Distribution of practitioners according to gender, age, and handicap.
Table 1. Distribution of practitioners according to gender, age, and handicap.
GenderMen (82.2%)
Women (17.8%)
AgeAverage: 47.58 age; Typical deviation: 12.51
<30 (11.1%)
31–40 (15.6%)
41–50 (29.4%)
51–60 (28.3%)
>60 (15.6%)
(Better players are those with the lowest handicaps)
Category 1
Category 2
Category 3
Category 4
Category 5
Category 6
≤4.4 (3.8%)
4.5 to 11.5 (19.2%)
11.6 to 18.4 (31.8%)
18.5 to 26.4 (17.7%)
26.5 to 36.4 (25.9%)
>36.4 (1.7%)
Without handicap (4.02%)/(Beginners)
Table 2. CFA. Goodness-of-fit indices for the causal covariance structure models.
Table 2. CFA. Goodness-of-fit indices for the causal covariance structure models.
Final Casual Model274.8473< 0.0013.760.960.950.970.960.960.053 (0.047–0.060)
* 90% confidence interval for the Root Mean Square Error of Approximation (RMSEA)
Table 3. Importance of different elements of golf club (Scale of 1–5).
Table 3. Importance of different elements of golf club (Scale of 1–5).
Average Typical Deviation
Involvement of Directive4.340.86
Professionalism management4.410.80
Professionalism reception4.310.78
Professionalism green keeper4.460.71
Professionalism master caddie4.420.73
Professionalism golf teacher4.550.71
Treatment and care provided4.580.67
Organization and management of club resources4.390.73
Information/communication management4.170.82
Complaints and suggestions management, fast and efficient4.120.87
Safety and risk prevention4.300.86
Environmental management4.350.73
Organization of tournaments in club4.430.73
Correspondence with other clubs4.340.81
Cleaning and general hygiene4.650.56
Physical state–maintenance of club facilities4.540.64
Physical state of furniture–equipment–club equipment4.350.74
Social hall-Clubhouse4.240.81
Changing room4.410.74
Golf Academy/School4.400.75
Maintenance/Course condition4.700.55
Control of play/Compliance with regulation in the course4.380.83
Design and round of the course4.440.69
Course security4.420.77
Practice zone4.380.72
Table 4. Analysis of simple variance for the comparison of the four groups.
Table 4. Analysis of simple variance for the comparison of the four groups.
Dependent VariablesFglSig.G1G2G3G4
Involvement of directive14.274967<0.0014.132.864.384.76
Professionalism management182.98967<0.0014.182.904.494.80
Professionalism reception145.69967<0.0013.853.364.364.79
Professionalism green keeper142.06967<0.0014.133.504.464.89
Professionalism master caddie113.63967<0.0014.063.674.394.87
Professionalism golf teacher82.94967<0.0014.124.104.564.92
Treatment and care provided125.92967<0.0014.193.734.694.90
Organization and management of club resources202.54967<0.0014.033.144.494.81
Information/communication management189.55967<0.0013.643.254.134.78
Complaints and suggestions management, fast and efficient219.95967<0.0013.423.234.164.74
Safety and risk prevention (emergencies)215.07967<0.0013.673.064.504.78
Environmental management117.38967<0.0013.793.824.464.72
Organization of tournaments in club72.54967<0.0014.163.774.394.82
Correspondence with other clubs62.14967<0.0013.953.724.384.69
Cleaning and general hygiene124.57967<0.0014.323.994.704.96
Physical state–maintenance of club facilities166.67967<0.0014.143.744.574.97
Physical state of furniture–equipment–club equipment250.61967<0.0013.843.354.354.92
Social hall–Clubhouse154.78967<0.0013.753.494.174.83
Changing room178.97967<0.0013.923.534.434.92
Golf Academy/School119.10967<0.0013.973.654.424.85
Maintenance/Course condition122.20967<0.0014.433.954.764.98
Control of play/Compliance with regulation in the course162.54967<0.0013.973.094.474.83
Design and round of the course78.50967<0.0014.113.964.394.83
Course security217.88967<0.0013.903.254.564.89
Practice zone136.34967<0.0013.963.714.334.88
Table 5. Contingency tables crossing the cluster of membership with different variables of interest.
Table 5. Contingency tables crossing the cluster of membership with different variables of interest.
Type of Installation (Course vs. Pitch & Putt)13.29<0.01
N° of Holes24.74<0.001
Relationship with the club42.86<0.001
Frequency of play34.47<0.01
Hours that play31.70<0.001
Main motivations45.15<0.05
Main barriers57.16<0.01
Who do you usually play with55.98<0.001
Travels to golf destination7.85<0.05
More used services68.76<0.001
Other practiced sports180.68<0.001
Academic training30.92<0.01
Table 6. Goodness-of-fit indices for the final causal covariance structure models for each segment.
Table 6. Goodness-of-fit indices for the final causal covariance structure models for each segment.
Segment I221.0773<0.0013.020.960.940.970.950.960.055 (0.047–0.063)
Segment II235.2072<0.0013.260.900.860.940.910.920.087 (0.075–0.100)
* 90% confidence interval for RMSEA.

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Serrano-Gómez, V.; García-García, Ó.; Gambau i Pinasa, V.; Rial-Boubeta, A. Characterization of Profiles as Management Strategies Based on the Importance and Valuation That Users Give to the Elements of the Golf Courses. Sustainability 2020, 12, 4781.

AMA Style

Serrano-Gómez V, García-García Ó, Gambau i Pinasa V, Rial-Boubeta A. Characterization of Profiles as Management Strategies Based on the Importance and Valuation That Users Give to the Elements of the Golf Courses. Sustainability. 2020; 12(11):4781.

Chicago/Turabian Style

Serrano-Gómez, Virginia, Óscar García-García, Vicente Gambau i Pinasa, and Antonio Rial-Boubeta. 2020. "Characterization of Profiles as Management Strategies Based on the Importance and Valuation That Users Give to the Elements of the Golf Courses" Sustainability 12, no. 11: 4781.

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