Motives and Points of Attachment of Professional Golf Spectators

Sport Management Review, 2004, 7, 167–192 © 2004 SMAANZ

Motives and Points of Attachment of Professional Golf Spectators Matthew J. Robinson University of Delaware

Galen T. Trail

University of Florida

Hyungil Kwon

Florida State University

Although professional golf has emerged as a leading spectator sport during the 20th century, there has been little research examining the consumption behaviour of those who attend tournaments across the three professional tours in North America. The purpose of this study was to examine whether the motives as measured by the Motivation Scale for Sport Consumption and points of attachment as measured by the Point of Attachment Index differed by gender and/or the tour event watched, after controlling for age and employment status. The relationship between motives and points of attachment was also examined. Data were collected at a PGA, an LPGA and a PGA Senior Tour event on each day of each tournament. A 2 (gender) × 3 (tour) multivariate analysis of covariance procedure on each of the areas (motives and points of attachment) was conducted. Finally, multivariate multiple regression analysis was used to predict a combined set of dependent variables (points of attachment) from a combined set of predictors (motives). The MANCOVA procedure for the motive factors indicated that the interaction effect was significant but the amount of variance explained was small. The multivariate analysis of covariance

Matthew J. Robinson is with the Department of Health and Exercise Sciences, Carpenter Sports Building, University of Delaware, Newark, DE 19716, USA. Galen Trail is with the Department of Tourism, Recreation and Sport Management at the University of Florida and Hyungil Kwon is with the Department of Sport Management, Recreation Management & Physical Education at Florida State University. E-mail for Matthew Robinson: [email protected]

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procedure for the motive factors indicated that the main effects of spectator and gender were significant as was the interaction effect but the amount of variance explained by each independent variable and the interaction was small. There was also a significant but small association between the dependent variables and the covariates of age and employment status. The MANCOVA procedure for the points of attachment factors also indicated that the interaction effect was significant but the amount of variance explained was minimal. The multivariate analysis of covariance procedure for the points of attachment factors also indicated that the main effects of spectator gender and tour were significant. The interaction effect was also significant but the amount of variance explained by each independent variable and the interaction was minimal. There was a significant but small association between the covariate of age but not between employment status and the dependent variables. The multivariate multiple regression procedure indicated that the motives were significantly related to the points of attachment and the variance explained was large. Specifically, vicarious achievement explained a moderate to large amount of variance in identification with a golfer, tour and hosting community. Based on all of this information, marketing plans do not need to differ based on the tour and the primary focus should be on a specific golfer or set of golfers who are playing in the event.

Robinson and Carpenter (2003) stated that golf has emerged as a popular spectator sport during the 20th century in North America as shown by the rise and success of the three major professional golf tours: the Professional Golf Association (PGA) Tour; the Ladies Professional Golf Association (LPGA) Tour; and the PGA Senior Tour. During the 2002 golf season, events on these tours drew over 10 million spectators and awarded over US$263 million in prize money to competitors (see Table 1). Marketing would be easy if those 10 million golf spectators were one homogenous group. However, environmental variables may indicate differences in spectator motivation for attendance and level of identification, thus indicating the potential for market segmentation based on those factors. Table 1: Attendance and Financial Statistics from Three Major Tours Variable

PGA

LPGA

Senior PGA

Number of events

46

32

34

Total attendance

7,200,000

1,700,000

2,500,000

Average purse

$4,500,000

$1,100,000

$1,500,000

$175,000,000

$36,000,000

$52,000,000

Total purse

Note: Data collected from personal interview with Ken Lovell, PGA Tour, and By the numbers (2003)

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The purpose of this study was threefold: to determine (1) whether gender and the tour event attended had an effect on motives after controlling for age and employment status, (2) whether gender and the tour event had an effect on points of attachment after controlling for age and employment status, and (3) whether motives had an effect on points of attachment. If PGA spectators differed from LPGA spectators and/or Senior PGA spectators, and if males differed from females on either motives or points of attachment, it would indicate that marketers should design marketing plans differently depending on the type of tour event and/or gender. However, if these demographic and situational variables explained a minimal amount of variance in the points of attachment, and motives explained more variance in the points of attachment, then it would be important for marketers to focus on motives for attendance as determinants for designing marketing plans, rather than environmental or demographic variables.

Difference Across the Three Major Golf Tours Along with the differences in attendance, number of events and prize money, the PGA, LPGA and PGA Senior Tour differ on a number of other levels. There are differences regarding the gender of the players (Senior PGA and PGA vs LPGA) and the age of the players (PGA and LPGA vs the Senior Tour). The tours also differ in the amount of media exposure each tour receives. Although all three tours (PGA, LPGA and Senior PGA) have events covered by cable networks, the number of events televised differs. The PGA had 34 events broadcast by major networks, while only five LPGA events received major network coverage, and the major networks broadcast only two Senior PGA events (By the numbers, 2003). Along with these elements related to the event itself, Crosset (1995) noted the homogeneous appearance of fans on the LPGA tour. They dressed the same, acted the same and there were no real distinctions based on seating. Based on experience the authors feel that Crossetʼs (1995) observation may be applicable across the three tours, however this certainly has not been proved. Thus, further investigation needs to be conducted to examine whether these groups are homogeneous on motives and points of attachment across the tours.

Motives for Spectating Cohen and Warlop (2001) suggested that although many marketing models are designed to predict attitudes and behavioural intentions, it is more critical to understand why consumers purchase particular products and services. Thus, some

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researchers have focused on the explanatory constructs at a higher level of a predicted motivational hierarchy. As Claeys and Vanden Abeele (2000) noted, one of the major paradigms used to explain the means-end chain theory of consumer behaviour is the motivational approach. They suggested that this approach “posits that the selfschema constitutes the frame of reference for the individualsʼ consumption behavior” (p. 363). Specific to sport spectating, Sloan (1989) noted that Harrisʼs (1973) broad somatopsychic theory generally states that individuals are attracted to sport spectating because of the reinforcing pleasures therein. Most of these pleasurable spectator or fan behaviours fulfill social or psychological needs, creating the self-schema of the means-end chain theory. Sloan proposed that most motives for spectating (to fulfill the needs) would fall under one of several theories: the salubrious effects theory, stress and stimulation theories, catharsis and aggression theories, entertainment theory, or achievement-seeking theories. This seems to illuminate the distinction between sport spectating consumption and the consumption of tangible products such as soft drinks or automobiles. Some of these theories suggested by Sloan may not be applicable to tangible product consumption, but are applicable to sport consumption because spectators tend to have higher involvement than many other consumers. For example, stress and stimulation-seeking do not motivate a majority of product consumption, although there certainly are exceptions, as automobiles may be for some people. As Sloan noted, it is Klausnerʼs (1968) contention that “only sports provide the means to create and experience those stresses in socially acceptable ways” (Sloan, 1989, p. 185). However, the idea of tracing consumption back to the motives and values of the individual, espoused by the means-end chain theory, has merit specific to sport consumption. Most of the research that has been done on motives has focused on the team sport environment. Researchers have examined differences in motives by gender (Dietz-Uhler, Harrick, End, & Jacquemotte, 2000; Fink, Trail, & Anderson, 2002; James & Ridinger, 2002; Wann, 1995; Wann, Schrader, & Wilson, 1999), type of sport attended (James & Ridinger, 2002; Robinson & Trail, 2003; Wann et al., 1999; Wenner & Gantz, 1989), stadium size and location (Nakazawa, Mahony, Moorman, & Hirakawa, 2000), race (Wann, Bilyeu, Brennan, Osborn, & Gambouras, 1999), age and economic factors (Pan, Gabert, McGaugh, Branvold, 1997), among others. However, typically in most of this research, although significant differences were noted, the meaningfulness of the differences (represented by effect size; see Cohen, 1988) could be questioned. Thus, segmenting sport spectator motivations based on socio-demographic differences may not be worthwhile for marketers because of the small amount of variance explained by the independent variables. Specific to golf, there is limited research examining the motives of attendees. McDonald, Milne, and Hong (2002) examined several team sports, including golf, and found that significant differences did exist between golf spectators and spectators of team sports on the motives of achievement, skill mastery, physical

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risk, aesthetics, aggression, value development, and self-actualisation. However, McDonald et al. (2002) did not speculate on why the differences may exist. Hansen and Gauthier (1994) recognised that spectators at the different tour events attended for different reasons. They found that attendees at PGA events enjoyed the scenery more than attendees on the other two tours. Attendees at LPGA events focused more on excitement and drama, shot-making finesse and the fitness benefits of walking the course than the attendees of the other tours. Attendees of the Senior Tour attended more for the big names and personalities of the golfers. These latter aspects are identification items or points of attachment.

Identification and Points of Attachment Claeys and Vanden Abeele (2000) indicated that relevant to means-end chain theory, consumption behaviour is predicated on situational and enduring involvement. Their definition of involvement is based on the work of Peter and Olson (1987) who stated that involvement is the personal relevance of the product or service to the individual. This is similar to the idea of identification, which has been used within the sport realm. Trail, Anderson, and Fink (2000) defined identification as “an orientation of the self in regard to other objects including a person or group that results in feelings or sentiments of close attachment” (pp. 165–166). This definition was based on the idea of a sense of self from Mead (1934) and Stone (1962). For example, Anderson and Stone (1981) argued that sports teams can become symbolic representations of a community and as such, they can provide individuals with a sense of belonging to that community. As this sense of belonging develops, the positive consequences of this association increase, fostering sentiments of group membership with other individuals. That same reasoning can be applied to an individual golfer. Legendary PGA golfer Arnold Palmer had a group that followed his rounds of play and were called “Arnieʼs Army”. Current PGA tour star, Tiger Woods, has a large following for his rounds as well. Various authors have shown that identification is a key predictor of sport consumption behaviour (Cialdini, Borden, Thorne, Walker, Freeman, & Sloan, 1976; Sloan, 1989; Wann & Branscombe, 1993; Zillmann, Bryant, & Sapolsky, 1989). Those authors and others have solely focused on identification with, or attachment to, a team. Trail, Robinson, Gillentine, and Dick (2003) identified other points, besides the team, to which an individual can attach, for example the coach, a specific player, a university or community. Again research has examined demographic and/or situational effects on team identification and points of attachment. Significant gender differences have been found on various points of attachment or on team identification (Fink et al., 2002;

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Funk, Mahony, Nakazawa, & Hirakawa, 2000; Wann & Wilson, 2001), however, other authors found none (Branscombe & Wann, 1991; Wann & Branscombe, 1993; Wann & Dolan, 1994). Other differences, by type of sport (James & Ridinger, 2002; Robinson & Trail, 2003; Trail, Robinson, et al., 2003), age (Wann, 2002), and education level (Wann, 2002), have been small or non-existent. Based on the above research, it is anticipated that if any demographic or environmental differences on the different points of attachment exist, the effect sizes will be small. If differences on points of attachment exist by type of sport, this would indicate that the salience of the role may be different for fans of the different professional golf tours. For example, PGA tour fans may be attached more to marquee names like Tiger Woods and Phil Mickelson than LPGA fans who may attach to the sport or the charities supported by the proceeds from the event. On the other hand, it is possible that sport is not a good indicator of specific points of attachment, and thus the salience of the role identities would not be sport type specific.

Relationships between Motives for Spectating and Points of Attachment Claeys and Vanden Abeele (2000) indicated that within means-end chain theory, motives are indicators of involvement. Iwasaki and Havitz (1998) depicted this relationship within their model of psychological commitment and behavioural loyalty. Recent research on sport spectators has also shown this link to exist (Fink et al., 2002; Trail et al., 2000; Trail, Fink, & Anderson, 2003; Trail & James, 2001; Wann, 1995; Wann, Ensor, & Bilyeu, 2001; Wann, Royalty, & Rochelle, 2002). Fink et al. (2002), Trail et al. (2000), and Trail, Robinson, et al. (2003), based on the research of Sloan (1989) and Maslowʼs (1943) hierarchy of needs, have shown that motives, particularly the need for vicarious achievement, is a predictor of, and antecedent to, team identification. Other research (Funk, Mahony, Nakazawa, & Hirakawa, 2001; Funk, Mahony, & Ridinger, 2002; Robinson & Trail, 2003) has shown that the appreciation of aesthetics was significantly correlated with interest in sport. These results indicate that different motives may differentiate among points of spectator attachment. Recently, Trail, Robinson, et al. (2003) in a study of football spectators, showed that the motives of skill, aesthetics, drama, and acquisition of knowledge were associated with the points of attachment of type of sport and level of sport. They also showed that the motive of vicarious achievement is associated with attachment to the team, coach, community and university. In their model, attachment to a player was not well represented by the second order latent variable termed organisation identification and thus was not related to vicarious achievement. However, because

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of the differences between team sport and individual sport spectating, it is expected that attachment to a player may be associated with vicarious achievement in this sample of golf spectators.

Golf Spectating and Team Sport Spectating The majority of previous studies on sport consumption behaviour have focused on team sports in a traditional viewing experience. Because minimal research has been published on golf spectating, prior research and models may not be applicable to a golf setting. This is supported by McDonald et al.ʼs (2002) findings that indicated that differences did exist between the motives of those classified as golf spectators and those classified as team sport spectators. Attending and viewing a golf tournament is different from attending traditional sporting events in that the spectator is watching specific individuals compete rather than teams. Robinson and Carpenter (2003) also noted that a spectator at a golf tournament can view the action from three different perspectives: they can stay at one hole and watch the field play through; follow a group, whether it be a pair, threesome or foursome, for 18 holes; or randomly walk the course, watching various golfers at various holes. Barkow (1989) stated that whichever viewing perspective is taken, the spectator will miss the majority of the action because of the four miles of arena and 150 players. This is quite different from the usual spectator experience where a fan is confined to a seat in an arena or stadium; and if the person stays in his or her seat, it is guaranteed that he or she will see the majority of the action in the contest (Hansen & Gauthier, 1993). Another aspect unique to a professional golf event is that competition is spread over four days. The standard format for a PGA and LPGA event is a 72-hole format where the players play 18 holes each day for four days. Not all players get to play all 72 holes. After the first 36 holes, those players who do not make the cut score exit the tournament and the remaining players continue to play on the final two days of the tournament. The winner is determined upon the lowest stroke total over the course of the four days of the event. There is no individual winner each day. In the case of a Senior Tour event, competition is spread over three days and there is no cut. For a spectator to see the entire event from start to finish, he or she would have to attend all four days in the case of the LPGA and PGA and three days in the case of the Senior Tour. Robinson and Carpenter (2003) noted that the usual spectator experience is confined to one day. The above-mentioned differences highlight that the golf environment differs from the traditional sport environment in terms of viewing and structure. Therefore, research is needed to explore the possible differences in spectatorship that might originate from differences in the nature of the event.

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Thus, the purpose of this study was to examine whether the motives and points of attachment for those who attend professional golf events differed by gender and/or the tour event watched, after controlling for age and employment status. Furthermore, the relationship between motives and points of attachment was examined.

Method Data were collected from spectators at events that are a part of the three major professional golf tours (PGA, N = 874; LPGA, N = 902; and Senior PGA, N = 528) during the 2002 golf season. Of the 2,750 questionnaires distributed, 2,304 usable questionnaires were returned, for a return rate of 83%.

Instrumentation

The questionnaire was comprised of two scales: the Motivation Scale for Sport Consumption (MSSC) and the Point of Attachment Index (PAI). In addition, the survey included items related to gender, age and employment status (full-time, part-time, retired, homemaker and not employed). The MSSC originally consisted of nine subscales (vicarious achievement, acquisition of knowledge, aesthetics, social interaction, drama/eustress, escape, family, physical attractiveness and physical skill). The MSSC has shown good internal consistency and construct validity in samples of Division I-A attendees (Fink et al., 2002; James & Ridinger, 2002; Trail, Robinson, et al., 2003) and in major league baseball spectators (Trail & James, 2001). Fink et al. (2002) recommended that the family scale be removed from the MSSC because spending time with oneʼs family was not necessarily a motive, per se, of sport consumption. In addition, for this study, the MSSC was slightly modified to improve on the previous inadequacies. Item 2 in the escape subscaleʼs wording was changed from the Trail and James study (the word “great” was removed from before the word “change”). In addition, the physical attraction subscale was deleted during the research on college athletics and not included for this study. The scale consisted of 21 items (seven subscales, three items per subscale) with a 7-point response format ranging from “Strongly disagree” (1) to “Strongly agree” (7). The Point of Attachment Index was originally created for research within college athletics (Robinson & Trail, 2003). The PAI has seven subscales focused on identification with (1) the players, (2) the coach, (3) the community, (4) the sport, (5) the university, (6) the team, and (7) the level of the sport (e.g., college, not professional). The PAI has shown good reliability previously (Robinson & Trail, 2003; Trail et al., in press). For this study the PAI was modified to focus on (1) the golfers, (2) the tour, (3) the sport, (4) the charities and (5) the community.

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Each subscale had three items and thus the PAI had 15 items total, with a 7-point response format ranging from “Strongly disagree” (1) to “Strongly agree” (7).

Procedures

The spectator intercept for golf method was used (Hansen & Gauthier, 1993). This method entails administering the surveys to spectators as they ride buses from the event to the satellite parking area or vice-versa. In this study, spectators were surveyed from the satellite parking area to the event. For the three tour events, all spectators were required to park in the satellite parking area and buses ran throughout the day during all four days of the event for the PGA and LPGA event and all three days of the Senior Tour event. The researcher selected a bus at the satellite area, distributed the survey to subjects as they boarded the bus and collected it as they exited. Subjects needed only to fill in circles to respond to questions, so completing the survey on the bus was not a problem.

Data Analysis

The RAMONA Structural Equation Modelling (SEM) technique, available in the SYSTAT 7.0 (1997) statistical package, was used to test the factor loadings of the items on the specified factors (a confirmatory factor analysis, CFA) on the pooled sample. The results (represented by measures of fit) indicate whether or not the model is plausible. In the case of CFA, the results allow the researcher to determine construct validity, both convergent and discriminant. That is, it is possible to determine whether the items represent the idea posited and whether each construct (idea) is distinct from another. The measures of fit used in the current study were Steigerʼs (1989); Steiger and Lindʼs (1980) root mean square error of approximation (RMSEA, a measure of discrepancy per degrees of freedom), and the test of close fit (Browne & Cudeck, 1992). The RMSEA is thought to alleviate problems associated with model fit that are not addressed by chi-square based statistics (Browne & Cudeck, 1992; Mulaik, James, Van Alstine, Bennett, Lind, & Stilwell, 1989), thus those indices are not included in the RAMONA statistical package. However, the chi-square value divided by the degrees of freedom was included as a frame of reference. The RMSEA value is bounded by zero on the lower end and will only be zero if the model fits exactly. Values less than 0.05 indicate that a model has a close fit, values of 0.08 or less indicate reasonable fit, and RMSEA values higher than 0.10 should not be considered (Browne & Cudeck, 1992). Hu and Bentler (1999) have recently suggested that values less than 0.06 should be used to indicate that a model has a close fit. Because the RMSEA value is a point estimate, it is also suggested that a 90% confidence interval be calculated and reported to show the level of confidence that the model would fit well within the population (Browne & Cudeck, 1992).

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In addition, for each scale or subscale, internal consistency measures (alpha coefficients) are reported to indicate how well the items correlate with each other on a specific scale. Values greater than 0.70 are assumed to be adequate. Construct validity measures (average variance extracted values, AVE) are indicated to show whether each item contributes to the scaleʼs underlying theoretical construct. AVE values above 0.50 indicate that the scales have good convergent validity (Fornell & Larcker, 1981). Once the construct validity and reliability are ascertained and the results indicate that they are adequate, it is possible to calculate mean scores for the factors based on the items and know that they are relatively accurate. A 2 (gender) × 3 (tour) multivariate analysis of covariance (MANCOVA) procedure was performed on each of the areas (motives and points of attachment). Adjustments were made for two covariates: age and employment status. Univariate tests (Tests of Between-Subjects Effects) provided with the MANCOVA analysis were used to to determine the specific relationships between the independent and dependent variables. Alpha levels were set at 0.01 because of the number of comparisons made and the sample size. Large sample sizes often cause any small difference to be regarded as statistically significant, thus Hair, Anderson, Tatham, and Black (1998) suggest examining effect sizes to determine practical significance. Furthermore, we were expecting effect sizes (η2) to be moderate to large to indicate meaningfulness (Cohen, 1988). As Cohen notes, a value of η2 = 0.01 is small, a value of η2 = 0.06 is moderate, and a value of η2 = 0.14 is large. Finally, a multivariate multiple regression analysis was used to predict a combined set of dependant variables (points of attachment) from a combined set of predictors (motives; Stevens, 2002). In addition, the multivariate tests allow the determination of the influence each motive subscale had on the combined set of identification subscales. Furthermore, the individual univariate tests indicate the specific relationships between each motive and each point of attachment. Again, alpha levels were set at 0.01 and effect sizes at η2 > 0.06.

Results Results of the confirmatory factor analysis on the MSSC (RMSEA = 0.059; CI = 0.057, 0.062; p < 0.001) indicated good fit (Hu & Bentler, 1999). Only 1.4% (3 out of 210) of the residuals were greater than 0.10, also indicating good fit (Bagozzi & Yi, 1988). All AVE values exceeded 0.50, ranging from 0.67 to 0.79 (see Table 2), indicating good convergent validity (Fornell & Larcker, 1981). All subscales indicated good internal consistency as Cronbachʼs alpha values ranged from 0.86 to 0.92 (see Table 2).

0.87 0.88

I feel like I have won when my favourite golfer wins

I feel proud when my favourite golfer plays well

0.91 0.84

I enjoy the natural beauty of golf

I enjoy the gracefulness associated with golf

0.75

I enjoy it when the outcome of the tournament is not decided until the final hole

Attending a golf tournament provides an escape for me from my day-to-day routine

0.78

0.81

I prefer watching a close tournament rather than one that is one-sided

Escape

0.90

I enjoy the drama of a close golf match

Drama

0.84

I appreciate the beauty inherent in the sport of golf

Aesthetics

0.81

β

I feel a personal sense of achievement when my favourite player does well

Achievement

Factor and item

0.77–0.80

0.73–0.77

0.80–0.82

0.89–0.91

0.83–0.86

0.89–0.92

0.83–0.85

0.87–0.89

0.86–0.89

0.80–0.83

CI

0.009

0.010

0.009

0.006

0.007

0.005

0.007

0.007

0.007

0.009

SE

82.43

71.85

94.74

151.00

118.80

175.10

115.70

121.90

120.40

92.85

t-value

0.89

0.86

0.90

0.89

α

0.73

0.67

0.75

0.72

AVE

Table 2: Factor Loadings (β), Confidence Intervals (CI), Standard Errors (SE), t-values, Cronbach’s Alpha Values (α), and Average Variance Extracted Values (AVE) for the Motivation Scale for Sport Consumption

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0.87 0.86

I increase my understanding of golf strategy by watching a tournament

I can learn about the technical aspects of the sport by watching the tournament

0.86 0.88

I enjoy watching a well-executed athletic performance

I enjoy a skilful performance by a professional golfer

0.85 0.90 0.90

I enjoy interacting with other spectators at golf tournaments

I enjoy talking with other tournament spectators

I enjoy socialising with the people near me at a golf tournament

Social

0.84

The athletic skills of professional golfers are something I appreciate

Physical skills

0.79

I can increase my knowledge about golf

0.90–0.92

0.90–0.91

0.84–0.87

0.87–0.89

0.85–0.87

0.83–0.85

0.85–0.87

0.86–0.88

0.78–0.81

0.88–0.90

0.89

A golf tournament provides a distraction from my everyday activities

Knowledge

0.87–0.90

CI

0.88

β

Attending a tournament provides a diversion from “lifeʼs little problems”

Factor and item

Table 2 (contd)

0.006

0.006

0.007

0.060

0.007

0.007

0.007

0.007

0.009

0.007

0.007

SE

163.60

162.20

122.40

150.10

128.70

116.30

118.20

124.90

86.20

137.00

132.50

t-value

0.92

0.89

0.88

α

0.79

0.74

0.71

AVE

178

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0.85 0.88

I am a big fan of specific golfers

I consider myself a fan of certain golfers on the tour

0.74 0.86

I would experience a loss if I had to stop being a fan of the [tour name] tour

Being a fan of the [tour name] tour is very important to me

0.85–0.88

0.87 0.84

I am a fan because the tournament enhances the communityʼs image

The reason I am a tournament fan is that it improves the nationʼs perception of the community

0.83–0.85

0.73–0.77

0.75

0.84–0.87

0.72–0.76

0.76–0.80

0.87–0.89

0.84–0.87

0.59–0.64

CI

I am attending the tournament because it increases the status of the local community

Identification with the community

0.78

I consider myself to be a real fan of the [tour name] tour

Identification with the tour

0.61

β

I identify with the individual golfers on the tour

Identification with a specific golfer

Factor and item

0.009

0.008

0.001

0.008

0.011

0.010

0.007

0.008

0.014

SE

96.77

107.30

67.82

109.10

66.47

78.65

121.10

108.50

42.43

t-value

0.86

0.83

0.81

α

0.67

0.63

0.63

AVE

Table 3: Factor Loadings (β), Confidence Intervals (CI), Standard Errors (SE), t-values, Cronbach’s Alpha Values (α), and Average Variance Extracted Values (AVE) for the Point of Attachment Index (PAI)

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0.76 0.77

Golf is my favourite sport

I am a golf fan at all levels and tours

0.70 0.93 0.92

The fact that the proceeds from the tournament go to a worthy charity is important to me

I attend the tournament because it is important to support charities associated with the event

Attending the tournament demonstrates my support for the charities associated with the event

Identification with the charity

0.83

β

First and foremost I consider myself a golf fan

Identification with sport

Factor and item

Table 3 (contd)

0.91–0.93

0.92–0.94

0.68–0.72

0.75–0.79

0.74–0.78

0.81–0.85

CI

0.006

0.006

0.012

0.011

0.011

0.009

SE

159.90

168.80

60.92

71.03

69.21

89.07

t-value

0.88

0.83

α

0.73

0.62

AVE

180

Robinson, Trail and Kwon

181

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The confirmatory factor analysis for PAI scale indicated reasonable fit (RMSEA = 0.080; CI = 0.077, 0.084; p < 0.001) according to Browne and Cudeck (1992). The residual matrix showed 8.6% of the residuals were greater than 0.10, which indicated good fit. The AVE values exceeded 0.50, ranging from 0.62 to 0.73 (see Table 3), indicating good convergent validity. The alpha coefficients ranged from 0.81 to 0.88 (see Table 3), indicating good internal consistency.

Motives

The MANCOVA procedure for the motive factors indicated that the interaction effect (gender by tour) was significant, Wilksʼ Λ = 0.982, F(14,3272) = 2.15, p = 0.008, η2 = 0.9%, but the amount of variance explained by the interaction was small according to Cohen (1988). There was a significant but small association between the covariates and the DVs: age, Wilksʼ Λ = 0.973, F(7,1636) = 6.40, p < 0.001, η2 = 2.7%; employment status, Wilksʼ Λ = 0.979, F(7,1636) = 4.96, p < 0.001, η2 = 2.1%. With the alpha value set at 0.01 for the univariate analysis, and after adjusting for differences on the covariates, the interaction of gender and tour on the motives of aesthetics, drama/eustress, and appreciation of physical skill was significant (see Figure 1; Table 4), but the variance was minimal (0.6%, 1.0%, and 0.7%, respectively). The univariate main effect of social interaction differed by tour, but again minimal variance was explained (1.5%). The estimated marginal means are reported in Table 5. Figure 1: Effect of gender × tour interaction on motives

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Table 4: Multivariate Analysis of Variance for Gender and Tour on Motives – Between-Subjects Effects Source

DV

df

F

p

η2

Gender × Tour

Achievement

2

4.25

0.014

0.005

Aesthetics

2

5.28

0.005

0.006

Drama

2

8.09

<0.001

0.010

Escape

2

1.87

0.155

0.002

Knowledge

2

3.33

0.036

0.004

Physical skills

2

5.55

0.004

0.007

Social

2

1.70

0.184

0.002

Escape

1

0.15

0.701

0.001

Knowledge

1

0.05

0.828

0.001

Social

1

2.44

0.118

0.001

Escape

2

3.13

0.044

0.004

Knowledge

2

2.24

0.084

0.003

Social

2

12.39

<0.001

0.015

Gender

Tour

Table 5: Estimated Marginal Means and (Standard Errors) of Motives by Gender and Tour Item

Sample

PGA

LPGA

SPGA

Motives 1. Achievement

4.21 (0.04)

2. Aesthetics

5.51 (0.04)

Males Females

5.61 (0.06) 5.23 (0.11)

5.47 (0.07) 5.61 (0.07)

5.66 (0.08) 5.50 (0.14)

3. Dramaa

5.84 (0.04)

Males Females

5.97 (0.06) 5.38 (0.11)

5.90 (0.07) 5.93 (0.07)

5.97 (0.08) 5.87 (0.14)

4. Escape

5.19 (0.04)

5. Knowledge

5.19 (0.04)

6. Physical skillsa

5.75 (0.04)

Males Females

5.74 (0.06) 5.37 (0.11)

5.83 (0.07) 5.96 (0.07)

5.79 (0.08) 5.82 (0.14)

7. Socialb

4.62 (0.04)

4.50 (0.07)

4.42 (0.06)

4.93 (0.09)

a

Notes: a Indicates gender × tour interaction; males on the first line of each cell under tour type and females on the second line b Indicates a main effect of Tour

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Points of Attachment

The MANCOVA procedure for the points of attachment factors indicated that the interaction effect was significant, Wilksʼ Λ = 0.968, F(10, 3276) = 5.37, p < 0.001, η2 = 1.6%, but the amount of variance explained by the interaction was minimal. There was a significant but small association between the covariate of age and the DVs, Wilksʼ Λ = 0.957, F(5,1638) = 14.72, p < 0.001, η2 = 4.3%, but not between employment status and the DVs at the 0.01 level, Wilksʼ Λ = 0.993, F(5,1638) = 2.24, p = 0.048, η2 = 0.7%. With the alpha value set at 0.01 for the univariate analysis, and after adjusting for the differences on the covariates, the interaction of gender and tour on the points of attachment of golfer and tour preference was significant (see Figure 2, Table 6), but the variance was minimal (0.8% and 1.9%, respectively). The univariate main effect of being attached to the sport of golf differed by gender (Table 6), but minimal variance was explained (2.6%). The univariate main effects of points of attachment to community, golf, and charity organisation, differed by tour (Table 6), but again minimal variance was explained (2.4%, 1.9%, and 0.9%, respectively). The estimated marginal means are reported in Table 7. Table 6: Multivariate Analysis of Variance for Gender and Tour on Points of Attachment – Between-Subjects Effects Source

DV

df

F

p

η2

Gender × Tour

Golfer

2

6.58

0.001

0.008

Tour

2

15.59

<0.001

0.019

Community

2

2.39

0.092

0.003

Sport

2

3.13

0.044

0.004

Charity

2

0.77

0.461

0.001

Community

1

0.12

0.724

0.001

Sport

1

44.08

<0.001

0.026

Charity

1

2.02

0.155

0.001

Community

2

20.07

<0.001

0.024

Sport

2

15.54

<0.001

0.019

Charity

2

7.75

<0.001

0.009

Gender

Tour

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Table 7: Estimated Marginal Means and (Standard Errors) of Points of Attachment by Gender and Tour Item

Sample

Males

Females

1. Golfera

4.83 (0.04)

4.96 (1.38)

4.75 (1.51)

2. Toura

4.40 (0.04)

4.52 (1.48)

4.42 (1.65)

5.33 (0.05)

4.74 (0.08)

PGA

LPGA

SPGA

Male Female

5.11 (0.06) 4.57 (0.12)

4.70 (0.08) 4.79 (0.08)

5.03 (0.09) 4.76 (0.16)

Male Female

4.68 (0.07) 4.05 (0.13)

4.20 (0.08) 4.56 (0.08)

4.65 (0.09) 4.29 (0.16)

3.58 (0.07)

3.63 (0.06)

4.25 (0.09)

4.68 (0.08)

5.15 (0.06)

5.28 (0.10)

4.36 (0.07)

4.72 (0.06)

4.61 (0.09)

Identification

3. Communityc 3.82 (0.04) 4. Sportb. c

5.03 (0.05)

5. Charity

4.57 (0.04)

c

Notes: a Indicates gender × tour interaction; males on the first line of cell under tour type and females on the second line b c Indicates a main effect of gender Indicates a main effect of tour

Figure 2: Effect of gender × tour interaction on motives

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Motives and Points of Attachment

The multivariate multiple regression procedure indicated that the combined set of independent variables (motives) was significantly related to the combined set of the dependent variables (points of attachment), F(35,9526) = 76.21, p < 0.001, and the variance explained was large (η2 = 0.64). In addition, the combined set of motives explained 42% of the variance in identification with a specific golfer, 40% of the variance in identification with a specific tour, 20% of the variance with the host community, 44% of the variance in being a golf fan, and 21% of the variance in identifying with the sponsored charity (see Table 8). The tests of between-subjects effects (see Table 9) indicated that vicarious achievement explained a large amount of the variance in identification with a specific golfer (12.8%) and tour (13.3%), and a moderate amount in community (5.9%). No other motive explained more than 5% of the variance in any one specific point of attachment. Table 8: Multivariate Multiple Regression for the Combined Set of Motives on Points of Attachment df

MS

F

p

η2

Golfer

7

278.90

235.88

0.001

0.421

Tour

7

298.92

216.76

0.001

0.401

Community

7

143.62

81.86

0.001

0.202

Sport

7

351.93

252.16

0.001

0.438

Charity

7

140.90

86.93

0.001

0.212

Table 9: Multivariate Multiple Regression for Motives on Points of Attachment – Between-Subjects Effects IV

DV

df

F

p

η2

Achievement

Golfer

1

333.54

0.001

0.128

Tour

1

347.32

0.001

0.133

Community

1

142.43

0.001

0.059

Sport

1

105.36

0.001

0.044

Charity

1

27.88

0.001

0.012

Golfer

1

8.91

0.003

0.004

Tour

1

43.05

0.001

0.019

Community

1

1.23

0.267

0.001

Sport

1

103.28

0.001

0.044

Charity

1

1.66

0.198

0.001

Aesthetics

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Table 9 (contd) IV

DV

df

F

p

η2

Drama

Golfer

1

54.66

0.001

0.024

Tour

1

0.51

0.471

0.001

Community

1

6.21

0.013

0.003

Sport

1

29.64

0.001

0.013

Charity

1

0.26

0.612

0.001

Golfer

1

0.37

0.544

0.001

Tour

1

1.43

0.232

0.001

Community

1

8.15

0.004

0.004

Sport

1

4.24

0.040

0.002

Charity

1

6.66

0.010

0.003

Golfer

1

3.02

0.082

0.001

Tour

1

14.91

0.001

0.007

Community

1

15.86

0.001

0.007

Sport

1

22.77

0.001

0.010

Charity

1

12.45

0.001

0.005

Golfer

1

27.57

0.001

0.012

Tour

1

10.59

0.001

0.005

Community

1

8.06

0.005

0.004

Sport

1

13.99

0.001

0.006

Charity

1

0.78

0.378

0.001

Golfer

1

5.74

0.017

0.003

Tour

1

0.99

0.320

0.001

Community

1

67.74

0.001

0.029

Sport

1

9.16

0.003

0.004

Charity

1

82.31

0.001

0.035

Escape

Knowledge

Skill

Social

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Discussion As stated in the introduction, the purpose of this study was to determine whether gender and the tour event attended had an effect on motives, after controlling for age and employment status, and whether gender and the tour event had an effect on points of attachment, after controlling for age and employment status. The study also examined whether motives were related to points of attachment. When a conservative alpha level was used, the results indicated that although there were significant group differences by the interaction of gender and tour type on motives as a whole, the amount of variance explained by the interaction was not meaningful according to Cohen (1988). The covariates of age and employment status also explained a minimal amount of variance in motives. Similar results were apparent for points of attachment. The interaction of the IVs, and age as a covariate, were significantly related to motives. Yet neither explained more than 4.3% of the variance, thus not exceeding the pre-established cut-off, nor meeting moderate effect size, according to Cohen. As expected however, motives explained a significant and large amount of variance in the points of attachment (64%), suggesting that motives seem to be a good indicator of identification and marketers perhaps should focus on motives when segmenting their markets. Specifically, vicarious achievement explained a moderate to large amount of variance in identification with a specific golfer, the specific tour, and the community hosting the event. These results are not particularly surprising; as recent results examining the effects of environmental and demographic variables on motives and points of attachment have shown minimal amounts of variance explained even when the differences were significant. Although Wann (1995) and Wann et al. (1999) found that gender explained approximately 10% of the variance in motives, more recent research with the MSSC (James & Ridinger, 2002; Robinson & Trail, 2003) has explained less than 4%. The lack of variance explained by type of tour also supports the findings of James and Ridinger (2002), Robinson and Trail (2003), and Wenner and Gantz (1989), in which motives did not differ by type of sport. Although McDonald et al. (2002) found differences in motives when comparing different sports, they did not find any significant differences when comparing levels of the same sport. For example, there were no differences on motives between college baseball and professional baseball, no differences between college basketball and professional basketball, and no differences between college American football and professional American football. The lack of variance explained by the demographic variables on the points of attachment was expected and supports the findings of Branscombe & Wann (1991), James and Ridinger (2002), Wann (2002), and Wann and Dolan (1994), among others. The relationship between motives and points of attachment supports the theoretical modelling of Claeys and Vanden Abeele (2000) within the marketing

188

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literature and Iwasaki and Havitz (1998) in the recreation literature. It also supports the findings within the sport literature, which indicated that vicarious achievement is related to identification. The present results differ to some extent because previous research has shown the relationship with team identification, whereas in this study, the largest relationship for vicarious achievement was with specific players and the tours on which they played. Previously, the relationship between vicarious achievement and attachment to a player was minimal at best (Robinson & Trail, 2003; Trail, Robinson et al., 2003). However, this should not be viewed with alarm; rather it demonstrates the uniqueness of the golf environment. There is no team with which to attach, so the person lives vicariously through the success of the favourite golfer to whom he or she attaches. Vicarious achievement was also associated with attachment to the community, which also supports the findings of Trail, Robinson et al. (2003) and their model. Still, marketers need to examine their own events relative to others on the tour or across tours before making specific decisions regarding marketing plans. However, based on these results, the preliminary indication is that marketing plans do not need to differ based on the type of tour event. Marketers can mix and match plans with impunity, or so it seems. This also seems to apply to gender, age and employment status differences. This is relevant to sport marketing firms (e.g., IMG, Octagon) who may be marketing several different tournaments across the three different tours. Marketers should instead focus on differences in motivations for attendance. The primary focus should be on the specific golfers who will be playing a particular event. It appears that this strategy is employed by the PGA tour events because of the strong name recognition of a number of its players. The LPGA has also moved in that direction as more of its players have increased their recognition (e.g., Annika Sorenstam, Se Ri Pak, Julie Inkster). The Senior Tour has relied on the name recognition of its players that was created while playing on the PGA tour. This plan may backfire if the focus is solely on one player, Tiger Woods for example, and he does not show or does not make the cut to play during the weekend. Perhaps a better plan is to identify several well-known players on the tour who should be attending, so as to broaden the scope of the marketing campaign. Marketers can also highlight the tour in its marketing focus on its traditions and the fact that in each case it is the highest level of golf offered in each playing category (male, female and seniors). In addition, because spectators were identified with their particular community, it would seem prudent to tie in the idea that by attending the spectator is also supporting the tour event within that community. This supports Crossetʼs (1995) study of the LPGA, which indicated the importance of an event to the community. The spectators are good representatives of the community and their presence indicates their support for bringing the event back in the future.

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There were limitations to the present study. Although the sample size was relatively large, spectators at only three events were sampled. In addition, even though the events were geographically disparate, other tour events may have spectators who are dramatically different from those surveyed in this sample. Further, this study examined only four different demographic and/or situational variables. Future studies could include other demographic variables such as income and race. Robinson and Carpenter (2003) examined situational factors such as how a ticket was acquired, amount of rounds of golf played in a year, number of days attending the tournament and the day attending the tournament. These factors could be examined in relation to motives and points of attachment. Finally, future research in the area of motives and points of attachment within the golf environment should expand the data collection to multiple sites on one tour to address the limitation mentioned above. Future research should also look at other individual sports (e.g., tennis, skiing, swimming) to see if the differences presented relative to vicarious achievement and attachment to the player are as strong as they were in the golf environment.

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