Mapping of intercollegiate sports relative to selected attributes as determined by a product differentiation strategy.

Pan, David W. ; Baker, John A.W.

Product differentiation is a marketing strategy used to influence consumers of a product's unique value, thereby giving it a competitive advantage over similar products (Porter, 1985). Essentially, a product is seen to have greater value in the eyes of consumers when its attributes can be compared more favorably than those associated with other products. If the product happens to be an intercollegiate sport event, it seems logical that athletic administrators would use differentiation strategies to determine and highlight the unique attributes associated with it to convince consumers of its values. The possession of specific knowledge of these attributes then would allow for the formulation of specific promotions to link the strategies to the needs of a target market, thereby capitalizing on the unique attributes associated with the sport. This would not only justify the inclusion of a particular sport to an athletic program, but also provide a rational basis for the improvement of strategies to promote the sport event's attendance.

Numerous studies concerned with factors influencing attendance at sport events have focussed on the affective polysemy of sport (Chalip, 1992); age of the franchise (Siegfried & Eisenberg, 1980); effect of the television telecast (Fizel & Bennett, 1989); game schedules (Hill, Medura, & Zuber, 1982; Zhang, Pease, Hut, & Michaud, 1995); per capita income or household income (Bird, 1982; Pan, Gabert, McGaugh, & Branvold, 1997); population size in the immediate locale of the event (Branvold, Pan, & Gabert, 1997; Siegfried & Eisenberg, 1980); ticket prices (Bird, 1982; Siegfried & Eisenberg, 1980); event promotion (Jones, 1984; Zhang et al., 1995); star players (Jones, 1984; Pan et al, 1997; Schurr, Wittig, Ruble & Ellen, 1987; Schwartz, 1973; Scully, 1974); substitution of the event for other forms of entertainment (Hill, Madura, & Zuber, 1982; Medoff, 1976; Pan et al, 1997); weather (Noll, 1974; Pan et al, 1997), and winning percentage (Branvold et al, 1997; Demmert, 1973; Jones, 1984; Noll, 1974; Scully, 1974; Whitney, 1988). Classification schemes of variables influencing attendance and involvement at sporting events and physical activities also have been derived by Chelladurai, Scott, and Haywood-Farmer (1987); Chelladurai (1992); and Hansen and Gauthier (1989). Further, an attempt to understand and subsequently develop an instrument to measure fan behavior has been made by Howard, Madrigal, and Kahle (1995).

Some of the aforementioned studies examined the effectiveness of factors or strategies on attendance and participation, or the socio-psychological characteristics of participants and attendees at a sporting event. Others were inferential in an attempt to predict spectators' behavior in terms of attendance at a sport event. They either used market segmentation strategies or focused on the variables influencing attendance at single sporting events. No research was conducted on the identification of the attributes associated with multiple intercollegiate sports sought by university students using product differentiation strategy. The authors believe that studies in this direction can answer questions of, for example, whether a wrestling competition is preferred over a track meet by university students seeking entertainment; whether students who attend a basketball game for reasons of excitement and crowd enjoyment will also attend a football game on a damp, cold afternoon for the same reasons; or whether reasons for attending sporting events are, in fact, the same for both males and females? It seems imperative that research should attempt to identify the attributes perceived by students to be associated with various sports and the magnitude in each sport to improve our understanding of why some have a greater appeal than others. This study was designed, therefore, to identify the unique attributes that attract students to a sport event using a product differentiation strategy, and subsequently test whether differences in the perception of these sports relative to the selected attributes exist between different groups of students, and then compare the positions of sports relative to the common factors of attributes as perceived by different groups of respondents. The specific purposes were to: (a) determine the common factors that prompt university students to attend a sporting event; (b) examine if perceptual differences toward each sport exist between different cohorts of respondents; (c) identify the position of each sport relative to the factors; and (d) assess the strength of the relationship between the factors associated with each sport.

Methodology

A list of 35 attributes considered suitable for prompting respondents to attend a sport event was derived form the works of Coakley (1994); Edwards (1973), Eitzen and Sage (1993), Figler & Whitaker (1991), Leonard (1993), Sloan (1989), and Yiannakis, McIntyre & Melnick (1993), and a survey of possible reasons for attending intercollegiate sport events identified by students in a pilot study conducted on the campus of an NCAA IA university in the Southwest region of the United States. This list was evaluated by a panel of researchers for content validity. based on four evaluative criteria (appropriateness, representation, suitability, and interpretability), some attributes were excluded and others modified until consensus was reached on 26 attributes. While numerous attributes were considered to be primarily present in participation sports in previous studies, the authors retained some of them for spectator sports in this study for their face values in attendance decision as indicated by most students in the survey. A questionnaire then was constructed in the form of a grid with a vertical list of the 26 attributes and an adjacent row of 15 intercollegiate sports (baseball, football, softball, wrestling, women's volleyball, men's and women's golf, basketball, gymnastics, tennis, and track and field) offered by an NCAA Division IA university. In addition, demographic information regarding the respondent's gender, race, year in college, and major area of study was also requested.

An enrollment profile of the students in terms of their gender, major area of study, and academic class status was obtained and examined prior to distributing the questionnaires to 217 undergraduate students enrolled in 10 randomly selected coeducational activity classes. No group of students was concentrated in a single area of study or with the same academic class status. Subjects were then asked to rate, according to a seven-point Likert-type scale ranging from 1 (to a low degree) to 7 (to a high degree), the importance of each attribute contained in each sport dictating their probability of attending that sport event, and placed that number in the appropriate place on the grid (Note: Subjects were informed that it did not matter if she or he had actually attended the sport event or not).

Data were first factor analyzed (for the means of each of 25 attributes on all sports, excluding the attribute of "Overall preference for attending the sport event") using a principal component extraction with an orthogonal rotation to identify the underlying factors of attributes respondents used to evaluate the sports. Data were then analyzed to determine if there was any difference in perceptions toward each of sports by gender (using t-tests) and academic class status (using one-way Analysis of Variance - ANOVA). Finally, data were analyzed using a Multidimensional Scaling approach titled Multidimensional Preference Analysis (MDPREF) which generated computerized 3-dimensional graphic presentations and corresponding data results.

MDPREF utilizes a point-to-vector model to derive perceptual maps that show stimuli points (i.e., the intercollegiate sports) in relation to vectors (i.e., the factors of attributes identified by the factor analysis). The model assumes a linear form such that a respondent's evaluation of the importance of attributes dictating the probability of attendance for a particular sport becomes stronger as it moves an infinite distance along the vector. To form the vectors, the program draws lines from the origin of a plot through the graph axis to infinity. The position of the sport then is projected onto the particular vector at an angle of 90 degrees. This projection shows the respondent's average perception of the sport relative to the attributes contained within each factor. The direction and proximity of the vectors suggest whether the sports have been evaluated differently (in this case by females and males). Sports possessing similar attributes are found in the same vicinity of the graph. In addition to providing the graphs, MDPREF also produces correlation coefficients showing the direction and strength of the relationship among the five factors. Because of the intersections of the vectors through the graph axis the MDPREF analysis does not provide the significance test that is usually associated with the traditional correlation coefficient analyses (e.g. Pearson Product Moment). Any disparity between the gender-paired coefficients suggests that the sports have been evaluated differently in terms of the attributes contained within these comparison factors.

Results and Discussions

A total of usable 172 (79%) grids were included in the data analysis. The respondents were considered representative of the campus where the study was conducted except for the fact that there was a lower percentage of female than male respondents (42:58) when compared to the university ratio (46:54) (see Table 1). This probably was due to the fact that activity classes were not required in the university's general education program.

Factor Analysis

The adequacy of the sample size was first confirmed using Kaiser's (1970) Measure of Sampling Accuracy (MSA). An MSA score of 0.92 indicated the size was "marvelous" according to the criteria descriptors proposed by Kaiser (1974) to judge the quality of the MSA. Using predetermined criteria of a factor's eigenvalue to be equal or greater than one; an attribute with a factor loading equal or greater than 0.50 without double loading; a factor having at least two attributes; and both a factor and loaded attributes being interpretable, four factors were identified explaining 71% of the eigenvalue proportion and comprising 18 attributes (see Table 2). The descriptors Sociopsychological Fulfillment (SF), Enthusiastic Commitment (EC), Recreational Incentives (RI), and Social Learning (SL) were given to the four factors based on the nature of the attributes contained within each. The internal consistency reliability for each factor was also tested and reported respectively. The attribute of Overall Preference (OP) toward each sport also was retained as a fifth factor to serve as a reference vector in the explanation of the graphic results from the MDPREF analysis.

t-Test and ANOVA Analysis

The mean scores of attributes in the Five factors aforementioned to each sport were tested for any differences in perceptions by gender and academic class status. Significant differences (p [less than] .05) were found between female and male perceptions of 7 of 15 sports (see Table 3). This justified the use of separate MDPREF procedures by gender for further analysis. No significant differences in perceptions toward sports between the cohorts of students with different academic class status were detected.

MDPREF Analysis

The MDPREF procedure generated a perceptual map illustrating the spacial position of each sport relative to the five factors of attributes, and a matrix of correlation coefficients for each gender group of respondents. The results of analyses were as follows.

Graph Results. Figure 1 shows the perceptual maps illustrating the position of each sport relative to the average score of importance in the attributes contained in each of the five factors as perceived by female and male students. For simplicity of discussion, only the graphs of two dimensions are presented. It can be seen on the female's map that relative to the vector OP, respondents clearly consider football (D), men's basketball (B), women's gymnastics (H), and baseball (A) in that order, as sport events toward which they showed a high degree of preference; followed to a lesser degree by women's basketball (C), men's gymnastics (G), softball (I), and women's volleyball (N). The remaining sports are clustered and less differentiated in terms of their positions relative to the vectors.

Table 1

Characteristics of Respondents

Demographics N %

Gender

Female 72 41.9
Male 100 58. 1

Race

African American 12 7.0
Asian American 29 16.9
Caucasian 115 66.9
Hispanic American 5 2.9
Native American 6 3.5
Multi-race 5 2.9

Year in College

1st year 21 12.2
2nd year 35 20.3
3rd year 48 27.9
4th year 42 24.4
5th year or more 26 15.1

Major

Physical Sciences 27 15.7
Social Sciences 16 9.3
Life Sciences 28 16.3
Education 6 3.4
Fine Arts 5 2.9
Engineering 10 5.8
Humanities 6 3.5
Business 16 9.3
Others 49 28.5
Undecided 9 5.2

[TABULAR DATA FOR TABLE 2 OMITTED]

Females perceived the attributes in the factors EC, SF and SL to be strongly present in the sports of football (D) and men's basketball (B), and the attributes in SL to be present to a strong degree in the sport of baseball (A). The attributes of the factor RI also were perceived to be most strongly present in baseball. These results suggest that females perceived the three "major sports" as those possessing the attributes in the aforementioned vectors to the highest degree in dictating possible attendance, with baseball being the best sport to furnish incentive opportunities.

Table 3

Means and Standard Deviations of the Evaluation of Men's
Intercollegiate Sports by Gender

 Female Male

Sport M SD M SD t

Baseball 3.84 1.38 4.02 1.37 -0.87
Basketball (women) 3.84 1.47 4.07 1.53 -1.03
Basketball (men) 4.13 1.35 4.80 1.21 -2.34(*)
Football 4.28 1.27 4.98 1.24 -3.57(**)
Golf (women) 2.55 1.66 2.70 1.67 -0.59
Golf (men) 2.57 1.68 2.83 1.71 -0.97
Gymnastics (women) 2.98 1.54 2.25 1.64 2.98(**)
Gymnastics (men) 3.05 1.51 2.27 1.64 3.20(**)
Softball 3.34 1.33 2.79 1.62 2.42(*)
Tennis (women) 2.91 1.37 2.36 1.43 1.92(*)
Tennis (men) 2.85 1.41 2.51 1.46 1.54
Track & Field (women) 2.65 1.54 2.47 1.78 0.71
Track & Field (men) 2.67 1.55 2.49 1.77 0.67
Volleyball 3.29 1.49 2.63 1.72 1.91(*)
Wrestling 2.78 1.61 2.75 1.78 0.11

* p [less than] .05, ** p [less than] .01

It is interesting to note that females displayed a strong preference for women's sport of gymnastics in addition to the three highly publicized men's sports of football, basketball and baseball. They also perceived that the attributes of EC to be strongly present in that sport, those of SF and SL only to a moderate and low degree respectively, and those of RI to be virtually absent. This might be explained by the popularity of women's gymnastics in the local area where numerous world class women gymnasts train and reside, and the fact that numerous gymnastic clubs have made their names out because of it. Female respondents appeared to consider women's gymnastics as a sport with a comparable high probability to attend as those "big-time" sports in terms of the attributes contained in the factors OP, EC, and SE but not in the vectors SL and RI. The remaining sports clearly lacked the attributes to a sufficient degree indicating these sports to be less desirable than those sports previously discussed.

On the male's map, respondents showed a high degree of overall preference toward football (D) and men's basketball (B), closely followed by baseball (A) and women's basketball (C); males also perceived those sports possessing a high degree of importance of the attributes in the factors RI, SL, SF, and EC in terms of dictating their attendance decision. Men's golf and softball appeared to be males' secondary choices of perceived preference, and the remaining sports were clustered and less differentiated at the beginning section of the vectors.

Male perceptions were similar to those of females in that they considered the attributes of EC, SF, SL and RI were strongly present in the sports of football (D), men's basketball (B), and baseball (A). The inclusion of women's basketball to the "major sports" by males may be due to the intensive promotional campaign of this sport by the NCAA in recent years, and the perceived higher degree of generic competitiveness in the sport as compared to other non"big-time" sports. The moderate positions of men's golf and softball may also indicate the tendency of attendance decision toward both sports as perceived by male respondents on all five vectors. The remaining sports were only perceived by males to possess the attributes to a low degree, indicating these sports did not possess a sufficient degree of motives along all vectors that may prompt attendance.

In general, these findings present a challenge to athletic personnel responsible for these medal intensive Olympic sports. To promote such sports to college students, marketers must change the perception of how they are viewed relative to the attributes contained in each vector. If they can convince students that unique attributes are inherent in a sport to a sufficiently high degree, then that sport will become distinctly attractive to a target group of students, thereby increasing the probability of attendance. Without such an educated differentiation of sports, present attitudes will probably be carried over into post-university life and subsequently could become the general feeling toward these sports by society.

Data Results. A matrix of correlation coefficients is presented in Table 4 showing the strength of relationships between factors used by female and male respondents to evaluate all intercollegiate sports. It can be seen that the correlation coefficients for females are generally lower than those for males, suggesting that females used more diversified underlying factors than those used by males in judgment.

The correlation coefficients of Overall Preference with the other vectors reveal the strength of the likelihood of individual factors prompting respondents to attend a sporting event, and subsequently provide the preponderant reasons that dominate their preferences toward a particular sport. Both female and male students perceived the OP highly correlated with the factor SF (e.g., "enjoy competition," "enjoy aesthetics of the sport," "enjoy spare time," "get excited in the event," "release stress," "substitute for a movie/theater," and "socialize with friends"). Males viewed OP also highly correlated with the EC (r = .9678, e.g., will attend even in bad weather, despite the distance, even if the time is inconvenient or ticket prices are high) and SL (r = .9552, e.g., "learn teamwork spirit by spectating, "learn how to handle win/loss in life," "learn how to obey rules in society," and "learn to build character"), whereas females viewed the OP highly correlated with the RI (r = .9519, e.g., opportunities to buy concession items, participate in promotional draw, and consume alcohol). Moreover, [TABULAR DATA FOR TABLE 4 OMITTED] females' OP also showed a weaker degree of tolerance relationship with the EC (r = .6190) than males'. These findings could provide an interesting rationale for college sport marketers: When formulating promotional strategies for individual sports, in addition to the communication of the attributes in SF inherent in the sport to all students, we have to promote to males by delivering the messages of social learning merited in the sport and eliciting the enthusiastic commitment of males, while emphasizing to females of the fun nature in RI, particularly when the event is held in a good weather, at a right time, with a short distance, and at a tolerable ticket price!

In summary, this study has identified four factors of attributes as perceived by respondents to be present in each of intercollegiate sports dictating the probability of attendance, and retained the Overall Preference as a reference vector. A gender difference in perceptions toward some sports were detected and consequently used as a justification for the use of separate MDPREF procedures for each gender group. MDPREF procedures revealed that three "major" sports were perceived to possess preponderant reasons in students' decision of attendance and preference. Moreover, females showed a strong interest in women's gymnastics while males in women's basketball over other non-major sports available. All respondents evaluated the overall preference of sports using the Sociopsychological Fulfillment as a common denominator, but differed to some extent in the remaining factors according to their respective gender background. The overall profile of assumptions used by males appeared to be more "linear" than that of females whose structure seemed to be more "complex" in their perceptual evaluations. The authors hoped that the findings can be used by college athletic administrators to formulate congruent marketing strategies to link with the needs of consumers in promoting non-major sports on alike campuses. Future studies without the inclusion of "major sports" in this direction are recommended thus probably making other sports become more differentiated and possession or lack of possession of pertinent product attributes more clearly identified.

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