An examination of sport commitment of windsurfers.

Jeon, Jung-Hwan ; Ridinger, Lynn L.

Participation in leisure and recreational sports is an important part of many people's lives. Half of the United States population regularly participates in sports. The total revenue of the U.S. sport industry is $213 billion with $71.3 billion being spent on sports-related equipment by recreational users alone (Eitzen & Sage, 2003). With the rapid growth of the sport industry, many new leisure and recreational sports have been created. This has allowed more opportunities for participants, but has also led to an increasingly competitive marketplace for each particular sport.

Windsurfing, one of the first extreme water sports, emerged in the early 1970's. With its unique and dynamic characteristics, windsurfing became popular worldwide and was selected as an Olympic event in 1984 for men and in 1992 for women (History of Windsurfing, n.d.). Windsurfing combines aspects of both sailing and surfing, along with certain athletic skills shared with other board sports like skateboarding, snowboarding, waterskiing, and wakeboarding. Windsurfing uses a sailboard that resembles a surfboard and is powered by a single sail. Steering a windsurfer is done by tilting and rotating the mast and sail as well as tilting and maneuvering the board. Windsurfing is possible in winds from near 0 to 50 knots, but the ideal conditions for most recreational sailors are when winds are at 15-25 knots. Windsurfing is enjoyed as both a recreational and competitive sport. Competitions in windsurfing include speed sailing, slalom, course racing, and freestyle events (Windsurfing, n.d.).

Windsurfing has a high aesthetic quality that can be enjoyed by spectators viewing from the shoreline, thus making it a sport that could contribute to beach tourism. Recently, however, the windsurfing industry has been confronted with a number of challenges, including competition from new water sports such as wake-boarding and kite-boarding, the loss of female windsurfers, and an overall decrease in the population of windsurfers (Bogucki, 2006; Ezzy, 2006; Wheaton & Tomlinson, 1998). The purpose of this research was to examine the sport commitment of participants in windsurfing. Specifically, this research investigated: 1) the validity of the Sport Commitment Model (Scanlan, Carpenter, Schmidt, Simons, & Keeler, 1993) for the population of windsurfers, 2) the relationship between predictor constructs and windsurfing commitment, 3) the relationship between windsurfing commitment and two outcome variables, participation frequency and purchase behavior, and 4) the differences in windsurfing commitment, participation frequency, and purchase behavior based on demographic variables (i.e., gender, age, income, and skill level).

The Sport Commitment Model

Scanlan, Carpenter et al. (1993) developed the Sport Commitment Model (SCM) to explore the underlying psychological characteristics of participants in various sports. They defined sport commitment as "a psychological construct representing the desire and resolve to continue sport participation" (p. 6). Their goal was to examine motivation factors leading to continued participation or commitment in organized youth sport. The SCM was based on ideas from both Thibaut and Kelley's (1959) social exchange theory and Rusbult's 0980) investment model.

To construct the components of the SCM, Scanlan, Carpenter et al. (1993) used three main dimensions labeled "causal conditions" by Kelley (1983). The "causal conditions," which either encourage or degrade the degree of commitment are: attraction, alternatives, and restraining forces. Scanlan, Carpenter et al. (1993) changed the terminologies of attraction and alternatives into sport enjoyment and involvement alternatives, and they divided restraining forces into involvement opportunities, social constraints, and personal investments.

Sport enjoyment is defined as a positive affective response to the sport experience that reflects generalized feelings such as pleasure, liking, and fun (Scanlan, Carpenter et al., 1993). It was suggested in previous research that enjoyment is the most important factor for both young athletes (Gould & Horn, 1984; Gould & Petlichkoff, 1988; Weiss & Petlichkoff, 1989) and elite athletes (Scanlan, Stein, & Ravizza, 1989) to continue in their sport. Scanlan, Carpenter and colleagues (1993) described involvement alternatives as the attractiveness of other activities that are a negative influence to a person's participation in a current sport program. Involvement opportunities are defined as valued opportunities that are present only through continued involvement. It is predicted that higher ratings of involvement opportunities will be related to greater sport commitment (Scanlan, Carpenter et al., 1993). Personal investments are defined as personal resources that are put into the activity which cannot be recovered if participation is discontinued, (e.g., time, effort, and money). In her study on close relationships, Rusbult (1988) suggested that investing numerous or sizable resources in close relationships would be important factors in commitment. Scanlan, Carpenter and colleagues (1993) defined social constraints as social expectations or norms that create feelings of obligation to remain in the activity.

Carpenter and Coleman (1998) suggested that social support should be added to the SCM. They defined social support as "the support and encouragement the athlete perceives significant others provide for their involvement in sport" (p. 198). People may feel more committed to a sport when they receive social support and related benefits such as being with friends, developing close relationships, and gaining recognition and social status through their sport participation (Allen, 2003).

Application of the Sport Commitment Model

The SCM was first used by Scanlan, Simons, Carpenter, Schmidt, and Keeler (1993) in youth sport domains. They applied the SCM to girls and boys participating in a Little League program. The results indicated that the survey instrument was a reliable scale for the youth sports domain and revealed that sport enjoyment and personal investments were the strongest predictors of participants' commitment.

Casper (2004) used the SCM with the addition of two outcome variables, participation frequency and purchase intention, to examine the sport commitment of adult recreational tennis players. The results of his study showed that tennis enjoyment, involvement opportunities, personal investments, and social support were positive indicators of tennis commitment while involvement alternatives and social constraints were inversely related to tennis commitment. Furthermore, he found that tennis commitment was associated with both participation frequency and purchase intention. Briefly, the participants who rated high in tennis commitment played tennis more frequently and were willing to spend more money for items related to their participation in tennis.

Alexandris, Zahariadis, Tsorbatzoudis, Grouious, and Thessaloniki (2002) tested the applicability of the SCM in the context of fitness participation in health clubs. In contrast to the many studies that showed enjoyment as the strongest factor for participants' commitment in both youth and adults, their research indicated that involvement opportunities were the most powerful predictors, followed by personal investments, enjoyment, and constraints. A possible explanation for this is that membership in health clubs may afford attractive or unique involvement opportunities that are associated with greater commitment.

The SCM has been applied to both youth and adult sport domains. However, no studies were found that applied this model to windsurfers. Thus, this study was undertaken to investigate the sport commitment of windsurfers and provide a better understanding of factors associated with continued participation in this unique, challenging, and aesthetically pleasing water sport.

Method

Participants

The respondents in this study were adult (18 years or older) windsurfers who participated in one of two windsurfing events; 1) the FriscoWoods WindFest on April 20-22, 2006, and 2) the Windsurfing Enthusiasts of Tidewater (WET) Spring Regatta on April 28-30, 2006. All levels of windsurfers, from professional to recreational, were represented in this sample. Convenience sampling was used to collect data during these two windsurfing events. Event participants were approached during their free time and asked to complete and return a survey about windsurfing. A cover letter was provided that explained the purpose of the study and assured confidentiality for respondents.

Instrumentation

The survey instrument included 20 items from the SCM questionnaire (Scanlan, Simons et al., 1993) and the modified SCM questionnaire (Casper, 2004).

Because the original SCM questionnaire was created for participants in the youth sport domain, some questions did not apply to adult participants and thus were not used. The selected questions were modified to pertain to adult windsurfers (Table 1). All items were measured using a five-point Likert-type scale. Based on Casper's study, participation frequency and purchase behavior were selected and modified for windsurfing participants. In the last section of the questionnaire, demographic information was collected in order to develop a profile of participants and run various analyses. The five predictor constructs of windsurfing commitment included windsurfing enjoyment (WE), involvement opportunities (IO), personal investments (PI), social constraints (SC), and social support (SS). One of the variables of the SCM, involvement alternatives, was excluded in this study because of reliability concerns revealed in previous research (Scanlan, Simons et al., 1993).

Results

Demographic Characteristics of Participants

A total of 139 usable questionnaires were analyzed. Of the 139 respondents, 110 (79%) were male and 29 (21%) were female. The participants in this study ranged in age from 18 to 71 years, the mean age was 43 (SD [+ or -] 11), and they had been involved in windsurfing for an average of 14 years (SD [+ or -] 8). Over half (57%) of the respondents indicated that their household income was in the range of $40,000-$80,000.

Validity of the Sport Commitment Model for the Population of Windsurfers

Factor analysis was conducted to examine the underlying relationships among the five predictor constructs. The KMO of the 16 items was .840, which is a high value to indicate that a factor analysis may be used with these data. Bartlett's Sphericity significance was .000, which also indicates the suitability of these data for factor analysis. All 16 items of these constructs (WE, IO, PI, SC, and SS) were entered into the factor analysis with principle extraction and Varimax rotation. Interestingly, the result of factor analysis yielded just two factors (Table 2). The first factor explained nearly 32% of the variance and included all items pertaining to WE, IO, and PI. This factor was called "intrinsic motivation" because it included 10 items that related to the positive personal desire to continue with involvement in windsurfing. The second factor contained all items of SC and SS, and explained nearly 18% of the variance. This factor was labeled as "extrinsic motivation" because items were associated with the influence of external others on one's continued participation in windsurfing. Cronbach's alpha scores indicated good internal consistency for all factor scales: intrinsic motivation ([alpha] = .86), extrinsic motivation ([alpha] = .72), and windsurfing commitment ([alpha] =.81) (Table 3).

Predictors of Windsurfing Commitment

Regression analysis was employed to examine the relationship between the predictor constructs and windsurfing commitment. The two factors revealed in the factor analysis, intrinsic motivation and extrinsic motivation, were used as the independent variables while windsurfing commitment was entered as the dependent variable. Intrinsic motivation, which included items pertaining to windsurfing enjoyment, involvement opportunities and personal investments, accounted for 68% of the variance of windsurfing commitment. The extrinsic motivation factor, containing items dealing with social constraints and social support, explained only 4.3% of the variance of windsurfing commitment (Table 4).

Participation Frequency and Purchase Behavior

Regression analyses were also used to investigate the relationship between windsurfing commitment and two outcome variables, participation frequency and purchase behavior. In this study, the average sailing days per year was 50 (SD [+ or -] 36). windsurfing commitment explained 15% of the variance of participation frequency (Table 5). The average amount of money spent by each participant on windsurfing involvement was $7,444. The regression result was significant (p < .01); however, as seen in Table 5, windsurfing commitment explained only 6.4% of the variance in purchase behavior of windsurfers.

Demographic Differences

The results based on demographic variables were varied. There was no significant difference in windsurfing commitment based on age. A significant difference, however, was found for gender t (136) = 2.50, p < .05. Male windsurfers had higher levels of commitment (M = 4.4) than female windsurfers (M = 4.0). Significant differences in windsurfing commitment were also found for income level F(5, 120) = 3.76,p < .01, and skill level F(3,134) = 13.21,p < .01. Post hoc analyses revealed that windsurfers in higher income brackets had higher levels of windsurfing commitment than those in lower income brackets. In addition, windsurfers who classified their skill level as either advanced or expert were more committed to the sport of windsurfing than those who labeled themselves as a beginner or intermediate.

For participation frequency, there were no differences based on age, gender or income. The only demographic variable that showed a significant difference was skill level F (3, 132) = 6.98, p < .01. The post hoc test revealed that there was a significant difference between lower levels of windsurfers including beginner and intermediate levels and higher levels of windsurfers containing advanced and expert levels. For example, expert windsurfers participated in windsurfing an average of 64 times a year, while intermediate windsurfers sailed an average of 37 times per year.

For purchase behavior, significant differences were found on two of the four demographic variables analyzed. There were no differences based on age or gender, but differences were evident for income level F(5, 100) = 2.54,p < .05 and skill level F(3,109) = 3.42,p < .05. The post hoc analyses showed that windsurfers in higher income brackets and those who were more highly skilled spent more money on purchases related to windsurfing.

Discussion

A factor analysis of the data revealed a two-factor structure. This differed from previous research that found five unique predictor constructs of sport commitment (Casper, 2004; Scanlan, Carpenter et al., 1993). The first factor was labeled "intrinsic motivation" and included all of the items comprising windsurfing enjoyment, involvement opportunities, and personal investment. This grouping of items was not completely unexpected based on a study by Carpenter, Scanlan, Simons, and Lobel (1993) that found significant correlation among the sport enjoyment, involvement opportunities, and personal investment components of the SCM. The second factor, labeled "extrinsic motivation," included the items from social constraints and social support. Many studies related to the motivation to participate in a sport activity have used the terms intrinsic motivation and extrinsic motivation to explain the reasons why participants continue their involvement (Deci, 1975; Pelletier et al., 1995; Pittman, Emery, & Boggiano, 1982; Vallerand & Bissonnette, 1992).

Of the two factors, intrinsic motivation had a much greater impact on windsurfing commitment than extrinsic motivation. The results indicated that intrinsic motivation accounted for 68% of the variance in windsurfing commitment while only 4.3% of the variance was explained by extrinsic motivation. This result is consistent with previous research that has shown intrinsic motivation to be more associated with greater persistence, positive emotions, greater interest, and sport satisfaction than extrinsic motivation (Pelletier et al., 1995). Furthermore, in previous research employing the SCM, the most powerful predictors of sport commitment were the three factors comprising intrinsic motivation in this study: sport enjoyment, involvement opportunities, and personal investments (Alexandris et al., 2002; Casper, 2004; Scanlan, Carpenter et al., 1993; Scanlan, Simons et al., 1993).

In regard to the four demographic variables examined in this study (i.e., gender, age, income level, and skill level), skill level was the one variable common to all three constructs being investigated: windsurfing commitment, participation frequency, and purchase behavior. Windsurfers who were more highly skilled had greater commitment to the sport and they participated more often. Only skilled windsurfers are able to navigate high wind conditions. Thus, it was no surprise that highly skilled windsurfers had higher rates of participation frequency since they have the ability to participate on more days under various weather conditions. Also, highly skilled windsurfers spent more money on windsurfing-related items. It is reasonable to assume that skilled windsurfers incur greater windsurfing-related expenses due to their frequency of participation, travel to windsurfing events, and their desire to have the most up-to-date equipment. The importance of skill was emphasized in Wheaton's (2000) study on the subculture of windsurfers. Wheaton found a connection between commitment and skill level and noted that windsurfing is a hard sport to learn, and to become a proficient windsurfer requires considerable commitment in time, effort, and money.

Implications for Practitioners

Skill level appears to be a key component to continued involvement with windsurfing. This finding, coupled with the result linking commitment with intrinsic motivation factors, provides those associated with water-sport organizations with knowledge that can assist in promoting the sport of windsurfing. Although the popularity of windsurfing has been declining in recent years (Bogucki, 2006; Ezzy, 2006; Wheaton & Tomlinson, 1998), practitioners involved in windsurfing-related businesses and associations can attempt to reverse this trend by applying information from this study to develop strategies that focus on skill development and create marketing messages that highlight intrinsic factors such as the enjoyment, pleasure, and unique experiences that can result form being involved with windsurfing.

To counter the decreasing population of windsurfers and attract new participants to the sport, windsurfing companies, local shops, and clubs or associations could extend free introductory windsurfing lessons to the public. Also, they could conduct seminars tailored to the various windsurfing skill levels to advance people to higher levels of expertise. In addition, windsurfing websites could be developed to provide instructional information, forums for dialogue on skill development, and free online videos that demonstrate windsurfing skills. Instructional DVDs as well as DVDs that feature the top windsurfers and windsurfing tricks could also be made available on these sites. Another idea for developing interest is to offer college physical education courses on windsurfing. Windsurfing clubs could partner with local colleges and universities to offer such classes.

Limitations and Future Studies

The present study is meaningful because it is the first to examine windsurfing through the Sport Commitment Model. However, this study only surveyed participants who competed in one of two windsurfing events. It is assumed that the participants already had a high degree of commitment to windsurfing. Therefore, the findings may not be generalized to all windsurfers. The adapted SCM questionnaire for windsurfing had a lengthy and slightly confusing section that asked about expenses associated with windsurfing. This measurement needs to be more clear and convenient for survey participants in any future studies. This study revealed the relationship between predictor constructs and windsurfing commitment; however, one of the original predictor constructs of the SCM, involvement alternatives, was not included in the present study due to concerns with reliability. Since kite-boarding is considered the most threatening alternative to windsurfing, future studies should focus on measuring the involvement alternative power of kite-boarding on windsurfers.

Findings from this study provided insight on the factors associated with sport commitment of windsurfers. It also revealed relationships between sport commitment and participation and purchase patterns. A better understanding of the factors associated with sport commitment of windsurfers may assist windsurfing-related businesses, associations, and enthusiasts with better meeting the needs and wants of this unique population of sport participants.

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Jung-Hwan Jeon

North Carolina State University

Lynn L. Ridinger

Old Dominion University

Address Correspondence To: Lynn L. Ridinger, Ph.D., Sport Management, Department of Human Movement Sciences, 2014 Student Recreation Center, Old Dominion University, Norfolk, VA23529, Phone: 757-683-4353, Fax: 757-683-4270, E-mail: Lridinge@odu.edu

Table 1. Items of the SCM Questionnaire for Windsurfers.

Constructs Items Questions

Windsurfing WC1 How dedicated are you to windsurfing?
Commitment WC2 How proud are you to tell other people that
 you windsurfing?
 WC3 It would be hard for me to quit windsurfing.
 WC4 Do you want to keep windsurfing?

Windsurfing WE1 Do you enjoy windsurfing?
Enjoyment WE2 Do you have fun windsurfing?
 WE3 Are you happy when you windsurf?

Involvement IO 1 Would you miss being considered a
Opportunities "windsurfer" if you stopped windsurfing?
 IO 2 Would you miss the people you windsurf with
 if you were to quit windsurfing?
 IO 3 Would you miss the goodtime that you have in
 windsurfing if you discontinued participation?
 IO 4 If you stopped windsurfing would you miss the
 unique experiences that you get by
 windsurfing?

Personal PI 1 I feel like I spend a lot of time on my
Investments windsurfing participation.
 PI 2 I feel like I put a lot of effort into my
 windsurfing participation.
 PI 3 I feel like I sped a lot of mommy on
 windsurfing.

Social SCl I feel that it is necessary to windsurf to be
Constraints with my friends.
 SC2 I feel that I windsurf to please others.
 SC3 I feel that I have to participate so others do
 not feel that I am a quilts.

Social SS1 People say things that make me feel good
Support about windsurfing.
 SS2 Other people encourage me to windsurf.
 SS3 Significant others (e.g., family or friends)
 say things to keep me windsurfing.

Table 2. Rotated Component Matrix of Items.

Items Factor Loading
 1 (Intrinsic 2 (Extrinsic
 Motivation) Motivation)

WE 2 .809
WE I .808
IO 4 .786
IO 3 .757
WE 3 .752
PI 2 .679
PI 1 .661
IO I .584
IO 2 .568
PI 3 .498

SC 2 .742
SC 3 .708
SC 1 .682
SS 2 .637
SS 3 .560
SS I .493

Table 3. Items Used for Scale Construction (N = 139)

Constructs Label # of [alpha] KMO
 Item

Windsurfing Commitment (WC) Dependent 4 .8l
 Variable
Windsurfing Enjoyment (WE) Intrinsic
Involvement Opportunities (IO) Motivation 10 .86 .880
Personal Investments (PI) Variable
Social Constraints (SC) Extrinsic
Social Support (SS) Motivation 6 .72 .728
 Variable

 Bartlett's
 Sphericity
 Significance

Windsurfing Commitment (WC)
Windsurfing Enjoyment (WE)
Involvement Opportunities (IO) .000
Personal Investments (PI)
Social Constraints (SC)
Social Support (SS) .000

Table 4. Regression Analysis for Variables Predicting Windsurfing
Commitment

Independent Variables [R.sup.2] [beta] F P

Intrinsic Motivation .68 .83 181.75 .000

Extrinsic Motivation .04 .22 7.08 .009

Table 5. Regression Analysis for Windsurfing Commitment Predicting
Participation Frequency and Purchase Behavior

Independent Variables [R.sup.2] [beta] F p

Participation Frequency 0.15 19.46 25.67 .000

Purchase Behavior 0.06 3346.52 8.88 .004