Factors Affecting Attendance at Professional Sport Events

Transcription

1 Journal of Sport Management Factors Affecting Attendance at Professional Sport Events Ha2 Hansen and Roger Gauthier University of Ottawa Forty attendance items comprised a questionnaire using a Likert 5-point scale to describe the relative importance of each factor from the view of the following sample of 117: CFL (B), NFL (28), NJ3L (21), NBA (23), MISL (ll), American Baseball (14), and National Baseball (12). It was hypothesized that no difference exists between leagues on attendance factors; on factor categories; between winning, moderately winning, and losing teams; and between indoor and outdoor leagues. ANOVA and Tukey tests were used for significant differences. Factor analysis using the principal component model followed by Varimax rotation was applied to the 40 items. The response rate was 46%. Significant differences resulted. Factor analysis derived 10 factor categories. Baseball and the NFL accounted for most of the differences, followed by the MISL. Items generating differences were scheduling, team roster quality, price, forms of entertainment competition, and convenience for fans. This study provides current status, factor categories, and preliminary trends that point to the need for further study with a larger sample. Factors affecting attendance at professional sport events are based in sport marketing. Personnel of pro teams package their products so as to increase attendance, and the effects of the factors can enhance a marketing plan. Any plan is premised on objectives, and one's understanding of factors affecting attendance is a key to developing a plan, strategies, and processes leading to the achievement of those objectives. In professional sport the increase of attendance is a key objective. Hence the importance of gaining knowledge about attendance factors. Schofield (1983) in a recent study of attendance factors reviewed 17 articles and developed four categories to account for those factors: economic, demographic, game attractiveness, and residual preference. Further, Schofield distinguished between production function studies and demand studies. The former focus on team performance and the influence of player skills on team output as measured by percentage wins or by statistics of player performance. The latter focus H. Hansen and R. Gauthier are with the School of Human Kinetics, University of Ottawa, 35 McDougal, Ottawa, Ontario KIN 6N5.

2 16 Hansen and Gauthier on the demand for sport entertainment as measured by attendance; factors such as price, population size, complementary commodities, consumer preference for sport, substitute forms of entertainment, and so forth are analyzed in relation to their effect on attendance. Production function studies focus on performance factors of players and team. Greenstein and Marcum (1981) analyzed team performance factors and attendance in National League Baseball in the years They expected a strong positive relationship between attendance and winning. Reasons for fans to attend games were hypothesized as the won-loss record of the team, the pitching staff, and the home run batters. Greenstein and Marcum found that 25% of the variance in attendance was due to team performance. Studies dealing with the effects of performance on attendance include those by Zech (1981), who focused on player performance factors, Porter and Scully (1982), who looked at managerial influence, and Scully (1974), who studied the effects of management morale and coaching. Scully suggested that four factors were important in determining player salaries: hitting or pitching performance, weighting of the players' contributions to team performance, number of years the player has spent in the majors, and the greater bargaining power of star or superstar players. According to Scully (1974, p. 917), "Gate receipts and broadcast revenue are directly related to the team's percent wins and the population of the area and indirectly related to player performance." Medoff, in studying player worth based on ability to attract fans to see the team win games, generated formulas to measure the team production function and found that baseball players were paid 30-50% of their worth. He commented that fans have a preference for the offensive contributions of players and cited differences between the American and National baseball leagues to justify this preference (1976, p. 117). A study of professional hockey by Jones (1984) found that "winning in its several forms and compensatory demand increasing strategies are statistically significant determinants of attendance but outcome uncertainty is not" (p. 62). Jones found a number of significant factors related to hockey attendance: a winning home team relative to the league, a successful visiting team relative to the league, the playoff drive as related to the importance of making the playoffs, the superstar player, and the preference for a fighting style team over a skating team. As reported by Schofield, a study by Zak, Huang, and Siegfried (1979) on basketball looked at field goal, free throw, and rebound percentages as well as turnovers and personal fouls, all of which significantly affect output. Schofield cites a study by Wilson (1980) on cricket wherein bowling and batting were strong influences on performance. Latham and Stewart (1981) studied objectives of the National Football League and their relationship to success of teams. They hypothesized that objectives were different for winning teams, moderately winning teams, and losing teams. These objectives in effect are factors that can affect attendance at professional sport events. To conclude production function studies, other than the data by Jones (1984), Schofield's words seem relevant: "There is little solid evidence beyond the obvious point that hitting and pitching skills (baseball) are important components of success... there is an obvious need for further investigation of these (basketball and cricket) and other sports to develop additional empirical evidence regarding production functions" (1983, p. 200).

3 Factors Affecting Attendance 17 In demand studies the focus is on reasons why fans attend games. Authors have pointed to many factors and shown how some have a significant effect on attendance. Attendance is strongly influenced by the degree of product differentiation achieved by the team (Fillingham, 1977, p. 81). Perceived superior skill level of athletes in a particular sport allows that sport to be designated as superior compared to other levels or leagues involved in the sport (Jones, 1969, p. 3). Professional sport leagues declare their product to be the best, in order to attract spectators willing to purchase tickets to view the product (superior athletes and teams). Demmert (1973, p. 10) says that attendance remains high when the outcome of the game is predictable in favor of the winning team. The rationale for this appears to rest with the relative benefits derived from vicarious association with the winner. The dimensions of the utility from the game involving two teams are (a) the uncertainty of outcome when the teams are well matched, which enhances the attractiveness to the fan, (b) the entertainment value wherein, regardless of each team's competitive level, the game is attractive because of the strategy involved, (c) the finesse of the athletes, (d) the nature of the sport, (e) the vicarious pleasure of relating to a winner, (0 the balance of competition within the league (Demmert, 1973; Drever & MacDonald, 1981 ; Jones, 1969; Noll, 1974), (g) the league standings effect, and (h) the media effect (Demmert, 1973). Fillingham (1977) lists several factors as contributing to attendance: a strong rivalry between teams, the record breaking performances of athletes, the presence of outstanding athletes, the cleanliness and accessibility of the facility, the scheduling of games, the geography factor in terms of climate being conducive to hockey and the proximity of the playing facility to the population, and ethnicity of players. Jones distinguishes among definitions of winning so that the outcome uncertainty variable is clarified. He points to a league-team conflict and explains that the league wants to maximize outcome uncertainty in order to maximize total profits. The teams, on the other hand, want profits as well and pursue a winning goal because fan attendance is related to winning. Jones suggests, "since neither maximizing uncertainty nor winning continually are viable goals, leagues and teams must devise alternative strategies to promote attendance" (1984, p. 54). Methods such as player distribution, rules adjustments, scheduling of games, creating league structures for competition (divisions and conferences), redefining winning to mean being a contender, making the playoffs, winning the division or conference, and using various promotions and price changes all help to resolve the dilemma of outcome uncertainty. Dawson, Malmisur, and Lewis (1984) analyzed aspects of professional soccer in America and England using Parson's paradigm. In terms of attendance, they commented on the need to satisfy the fans: The crowd spectacle at soccer matches has much in common with American sport in general (cheerleaders, instant replays and special give-away games). In addition, the owners of clubs have developed stadiums in the U.S.A. with comfort as the central concern, and profit the long term goal. Fans are all seated in the stadium; many facilities are air conditioned; there are large adjacent parking lots. All this adds to the service element for spectators. (1984, p. 102)

4 18 Hansen and Gauthler The categories developed by Schofield for demand studies are similar to those of other authors; for example Hart, Hutton, and Sharot (1975) listed economic, geographic, and demographic factors and the fans' perception of the entertainment of the game; Greenstein and Marcum (1981) outlined sociodemographic, accessibility, and performance variables. Groupings of variables seem appropriate based on the number of individual factors that various authors have studied. However, different categories of factors could emerge as a result of the present study. The literature may be summarized under four general categories of factors: economic, sociodemographic, attractiveness of game, and residual preferences. In the economic category, studies have related to ticket price (Bird, 1982; Demmert, 1973; Fillingham, 1977; Noll, 1974; Siegfried & Eisenberg, 1980), per capita income (Bird, 1982; Hart et al., 1975; Noll, 1974), substitute forms of entertainment (Demmert, 1973; Hart et al., 1975; Hill, Madura, & Zuber, 1982; Medoff, 1976; Noll, 1974), television effects (Demmert, 1973; Drever & MacDonald, 1981; Hill et al., 1982), and the effects of other sports attractions in the area (Demmert, 1973; Fillingham, 1977; Hart et al., 1974; Hay & Thueson, 1986; Hill et al., 1982; Medoff, 1976; Noll, 1974). The latter three types of factors have been found to negatively affect attendance of baseball games. Ticket price may raise revenue but is directly related to demand as well as other factors. The sociodemographic category of studies focuses on population size of area (Fillingham, 1977; Hart et al., 1975; Hay & Thueson, 1986; Medoff, 1976; Noll, 1974; Siegfried & Eisenberg, 1980), ethnic population (Fillingham, 1977; Medoff, 1976; Noll, 1974; Scully, 1974; Siegfried & Eisenberg, 1980), and geography (Drever & MacDonald, 1981; Fillingham, 1977; Greenstein & Marcum, 1981). Population size has a positive effect on attendance; however, the presence of ethnic groups in the population may have a negative effect on attendance. Geography refers to distance between franchises, conducive climate related to sport, and easy access to facility for fans-all of which have a positive effect on attendance but a negative one for rugby. The category designated as attractiveness of the game contains several types of studies, all of which have a positive influence on attendance. The one exception relates to the closeness of the pennant race, and here more analysis is needed to verify the positive influence on attendance. Factors in this category are related to promotions and special events (Hill et al., 1982; Jones, 1984; Siegfried & Eisenberg, 1980), star players (Fillingham, 1977; Hill et al., 1982; Jones, 1984; Medoff, 1976; Noll, 1974; Scully, 1974), the team as a contender or team placement in the standings (Bird, 1982; Hart et al., 1975; Hay & Thueson, 1986; Hill et al., 1982; Siegfried & Eisenberg, 1980), and the closeness of the pennant race (Demmert, 1973; Drever & MacDonald, 198 1; Hart et al., 1975; Jones, 1969, 1984; Noll, 1974). The fourth category, residual preference, deals with scheduling of games, fan accommodation, and so forth. Specifically, weekend games and end-of-season games increase attendance, afternoon games decrease attendance, and double headers and home dates seem to have no effect on attendance (Drever & MacDonald, 1981; Fillingham, 1977; Hay & Thueson, 1986; Hill et al., 1982; Siegfried & Eisenberg, 1980). A positive effect on attendance applies to the new stadia and accessibility category (Demmert, 1973; Fillingham, 1977; Greenstein & Marcum, 198 l; Hay & Thueson, 1986; Hill et al., 1982; Medoff, 1976; Noll, 1974;

5 Factors Affecting Attendance 19 Scully, 1974; Siegfried & Eisenberg, 1980). The length of time a franchise has been in the area has a positive effect on attendance (Demmert, 1973; Siegfried & Eisenberg, 1980). The last category of studies within residual preferences is weather conditions, wherein results have been found to be both positive and negative (Bird, 1982; Drever & McDonald, 1981; Noll, 1974; Siegfried & Eisenberg, 1980). Purpose of Study The present study sought to expand knowledge of factors that affect attendance at professional sport events in North America. The literature review was limited to professional sport and North American leagues. In addition, limited data were derived from people directly associated with professional sport. Hence, to shed more light on the effects of various factors affecting attendance, this study sought the views of the head of marketinglpromotion of teams comprising six professional sport leagues in North America. The relative importance of each of 40 factors was determined so as to identify clusters of attendance factors that differed from the literature as well as to ascertain the differences between leagues on each factor and on clusters of factors. Also considered was whether there are differences between winning teams, moderately winning teams, and losing teams, and whether there are differences between indoor and outdoor leagues on the factors affecting attendance. These factors should be studied further and, as Schofield said, "it is important to build up further evidence regarding the relative importance of the factors which are now fairly well established as significant... further there are certain professional sports which have not yet received any attention at all" (1983, p. 204). Hypotheses 1. That no significant difference exists between leagues on items representing the effect on game attendance and items representing the effect on season attendance. 2. That no significant difference exists between leagues on the effect of attendance on game factors and the effect of attendance on season factors. 3. That no significant difference exists between winning teams (.500+), moderately wi~ing teams ( ), and losing teams ( ) on the effect of attendance on game factors and the effect of attendance on season factors. 4. That no significant difference exists between indoor leagues (NHL, MISL, NBA) and outdoor leagues (NFL, CFL, baseball). Methodology The 40 factors making up the questionnaire were derived from the literature (see Table I). A 5-point Likert scale was used to ascertain the relative importance of each factor related to its effect on a per game basis and on a per season basis. Questionnaires were sent to the head of marketing/promotion of each team in each of the following leagues: Canadian Football League (n = 8), National Football League (n =28), National Hockey League (n =2 l), National Basketball As-

6 Hansen and Gauthier Table 1 40 Factors Derived From the Literature Economic Factors Television coverage of the home game in local area Price of season ticket for home games Television coverage of another major sport event at time of your home game Price of ticket for home game Price of other forms of entertainment available during your games Existence of other sport teams in your area Average income of population Other professional franchises in your area Demographic Factors Population size of your area Ethnic mix of population Existence of minor league sports for children and youth Attractiveness Factors Record (won-loss) of home team Number of star athletes on visitor's roster Offensive output of your team (goals, points, T.D.s, home runs, stolen bases, number of yds. total, yds. passing, etc.) Number of star athletes on your roster (home team) Closeness of competition (between teams during season) Record (won-loss) of visiting team - Your team's involvement in race for 1st place Rivalry between your team and opponent (visiting team) Defensive output of your team (goals against, defensive line, secondary, pts. against average, number of sacks, number of steals, E.R.A., etc.) Record breaking performances of athletes on visiting team Record breaking performances of athletes on home team Special event occasions (bat day, special groups day, etc.) Your team's place in the league standings Your team's place in the division standings Your team's involvement in race for a playoff spot Residual Preferences Factors Afternoon game First quarter of the season Cleanliness of the facility Behavior of fans during games Easy andlor multiple access to your facility (via subway, highways, transit) Evening games Second quarter of the season Availability of parking at or near facility Unobstructed view of game for 80% or more fans Size of the facility (seating capacity) Weekend games (Friday night, Saturday andlor Sunday) Number of years your franchise has been in the area Third quarter of the season Fourth quarter of the season

7 Factors Affecting Attendance 2 1 sociation (n=23), Major Indoor Soccer League (n=ll), American Baseball League (n= 14), and National Baseball League (n= 12). Reminder letters with questionnaires were sent to nonrespondent teams within 1 month of the initial questionnaire being mailed. The total sample was 117. The criteria for performance levels of teams were based on the Latham and Stewart (1981) study in addition to accepted levels of performance operating in the environment of professional sport. Thus, winning teams have a season performance record of.500 or better, moderately winning teams have a performance record of , and losing teams are Statistical Treatment Descriptive statistics were generated for the 40 items related to factors affecting attendance in terms of per game effect and per season effect. Similarly, data were generated for performance of teams and leagues in terms of winning teams.500+, moderately winning teams, , and losing teams Analysis of variance (ANOVA) was used to test for significant differences between leagues per attendance factors related to game effect and season effect, as well as the teams' performance levels (winning, moderately winning, losing). ANOVA was also used to test for differences between leagues on factors derived for game and season factors, and for indoor and outdoor leagues. When the analysis indicated significant I; values, the Tukey test was applied to determine where significant differences existed. Responses to each questionnaire item for items affecting attendance related to per game effect and for season effect were factor analyzed using the principal component model followed by varimax rotation (Nie, Hull, Jenkins, Steinbrenner, & Bent, 1975). The criteria used for the selection of items constituting each factor were (a) that each item should have its highest loading on the same factor, and (b) that each item loading should be higher than.40. Results and Discussion The application of performance percentages resulted in 29 winning teams, 15 moderately winning teams, and 10 losing teams. The sample respondents was 54 of 117, or a 46% return rate overall. The sample breakdown was as follows: CFL, 6 of 8; NFL, 14 of 28; NHL, 9 of 21; NBA, 6 of 23; all baseball, 11 of 26; MISL, 8 of 11. Table 2 presents descriptive statistics for each factor, derived from the literature, that affects attendance on a per game and per season basis. Given the criterion of >1.00 for standard deviations as related to legitimacy of items, it is noted that the majority of factor items exceed this level. The sample size responding to each item is the main reason for the relatively high standard deviations. The literature review derived four categories that are illustrative of factors affecting attendance: economic, 8 items; demographic, 3 items; attractiveness of game, 15 items; and residual preferences, 14 items (Table 1). Items generating means greater than 3.15 amounted to 25 of the total of 40 that respondents cited as important on the 5-point Likert scale (see Table 3). Items were broken down by the magnitude of their respective means into four groupings: items with M>4.00=7; items with M>3.67<4.00=6; items with M> =6; items with M>3.15<3.40= 10. Clearly respondents assigned a definite level of impor-

8 22 Hansen and Gauthfer Table 2 Descriptive Statistics Per Attendance Item Attendance item Sample M per Sample M per size game size season n effect SD n effect SD Television coverage of the home game in local area Price of season ticket for home game Television coverage of another major sport event at time of your home game Price of ticket for home game Price of other forms of entertainment available during your games Existence of other sport teams in your area Existence of other major league professional franchises in your area Average income of population Population size of your area Ethnic mix of population Existence of minor league sports for children and youth Afternoon game First quarter of the season Cleanliness of the facility Behavior of fans during games Easy andlor multiple access to your facility (via subway, highways, transit) Evening games Second quarter of the season Availability of parking at or near facility Unobstructed view of game for 80% or more of fans Size of the facility (seating capacity) Weekend games (Friday night, Saturday andlor Sunday) Number of years your franchise has been in the area Third quarter of the season Fourth quarter of the season (cont.)

9 Factors Affecting Attendance 23 Table 2 (cont.) Sample M per Sample M per size game size season n effect SD n effect SD Record (won-loss) of home team Number of star athletes on visitor's roster Offensive output of your team (goals, points, T.D.s, home runs, stolen bases, number of yds. total, yds. passing, etc.) Number of star athletes on your roster (home team) Closeness of competition (between teams during season) Record (won-loss) of visiting team Your team's involvement in race for 1st place Rivalry between your team and opponent (visiting team) Defensive output of your team (goals against, defensive line, secondary pts. against average, number of sacks, number of steals, E.R.A., etc.) Record breaking performances of athletes on visiting team Record breaking performances of athletes on home team Special event occasions (bat day, special groups day, etc.) Your team's place in the league standings Your team's place in the division standings Your team's involvement in the race for a playoff spot tance to items reflecting a positive effect on attendance per game. In summary, only 13 items attained a definite perceived importance level while the remaining 15 items showed some positive effect on attendance by being greater than The respondents, presumably actively involved in the operation of professional franchises, indicated the perceived relative importance of specific influences on attendance per game and per season. With the exception of population size, average income of population, and price of game and season tickets, items falling in the categories of attractiveness and residual preferences were clearly deemed

10 24 Hansen and Gauthler important. The items most responsible for increasing attendance on a per game and per season basis were those concerning the team's involvement in competition as well as the quality of the team in relation to opponents. Table 3 summarizes the items and their means in descending order, from a high of 4.63 to a low of Items dealing with economic and sociodemographic aspects were generally considered as unimportant influences on attendance. Factor Analysis This study generated 10 factor categories for items affecting attendance on a per game and per season basis (see Table 4). The items loaded differently on the 10 factors per game as compared to per season. The difference of categories between the literature (with three or four) and this study can be accounted for by the specificity of the 40 items and the fact that this study was conducted to fil a void in the literature. The existence of 10 factors with items of varying loading magnitude gives us additional information to consider when compared with the literature. Experts in the field involved with their respective teams are the ones who assign importance to the items that make up the factors. This hands-on field perspective to items increasing and decreasing attendance sheds light on this question, but further research is needed to verify these item loadings and factor categories. Enlarging the sample size to include Division I universities in the United States as well as getting a higher professional team response rate would give us a more complete picture. Table 3 indicates that the majority of items (19) are loaded on the first three categories, as presented in Table 4. Factor 1 has four of seven items related to the schedulingof games, and Factor 2 has four of seven items focused on the convenience of fans (e.g., parking and cleanliness). Factor 3 has four of five items related to the performance quality of the team. Thus the 19 items of three factors related to three thrusts: scheduling, convenience, and quality of performance. Factor 4 has all items relating tothe team as a contender. ~hese items received the highest means (Table 3). Factor 7 has similar items to Factor 4 and also has high means. Factors 6 through 10, although specific to a few items, for example scheduling and price, have for the most part a mix of items. Since Factors 4 and 7 contain the same items, logically they should have loaded on the same factor category. Perhaps further research will provide a clearer picture. The per season loadings in Table 4 follow a similar pattern, with Factor 1 containing all items concerned with scheduling. Four of six items contained in Factor 4 relate to game quality. Factor 5 is concerned with the team as a contender but has only three items, all of which received high levels of importance (Table 3). These mixed loadings with hints of trends essentially cannot be titled in a fashion similar to the literature review. Further study of these items per factor category involving a larger sample could consolidate items so that one may identify categories of factors that increase or decrease attendance on a per game and per season basis. In summary, it would appear from the data in Table 4 with reference to Table 3 that attendance items related to the categories of attractiveness and residual preferences are considered more important as factors that positively affect attendance at professional sport events.

11 Factors Affecting Attendance Table 3 Relative Importance of Attendance Items Attendance item Per game M Per season M Team's involvement in playoff race Team's involvement in 1st place race Team's place in division standings Team's place in league standings Rivalry of team and opponent over season Record of home team Weekend games Closeness of competition during season Record breaking performance of home team and athletes Unobstructed view of game for 80% or more of fans Fourth quarter of season Star athletes on home team roster Population of area Evening games Record (won-loss) of visitor team Offensive output of home team Star athletes on visitor team roster Special event occasions Availability of parking Easy access to facility Third quarter of season Average income of population Cleanliness of facility Record breaking performance of visitor athletes Behavior of fans during games Years franchise has been in area Price of ticket for home games Defensive output of home team Price of season ticket for home games League Differences Table 5 presents data related to league differences on each of the 40 items affecting attendance on a per game and per season basis. It is noted that only 13 items on a per game basis and 6 on a per season basis attained a level of significance greater than.05, and that baseball differs from the NFL on 9 items and the NFL differs from other leagues on 12 items. Table 3 reflects the relatively low level of importance placed on price of tickets by the total sample, yet Table 5 presents this item as significant in terms of the MISL and NHL, both of which are indoor sports using smaller facilities compared to baseball and football leagues.

12 Table 4 Effect of Attendance Factors on Game and Season and Their Loadings on 10 Factors Per games Per seasons Attendance effect item Evening games Unobstructed view (80%) Weekend games Offensive of home team Fourth quarter of season Third quarter of season Years franchise! in area Average income of population.87 Easy access to facility.80 Ethnic mix of population.71 Availability of parking.64 Seating capacity of facility.61 TV coverage of another major.46 sport during home game Cleanliness of facility.45 a Record breaking performance of visitor athletes 3 Record breaking performance of a Ja home athletes Rivalry of team and opponent Defensive output of home team Special event occasions C 2 cont. S

13 Table 4 (cont.) 9 Per games Per seasons Attendance effect item $ V) Team's place in division standings Team's place in league standings Team's involvement in 1st place race Team's involvement in playoff race TV coverage of home game in area Star athletes on home roster Star athletes of visitors First quarter of season Other pro franchises in area Other sport teams in area Price of other entertainment forms in area Closeness of competition during.n season Record of visiting team Record of home team Minor sport (youth) in area Population size of area Behavior of fans at games Second quarter of season Afternoon game Price of season ticket Price of ticket to home game

14 Hansen and Gauthier Table 5 Analysis of Variance on Attendance Factors F Significance Attendance factor ratio level Leagues involved Per Game Basis Evening games Weekend games First quarter of season Second quarter of season Third quarter of season Fourth quarter of season Record breaking performance home athletes Special event occasions Cleanliness of facility Price of ticket for home game Minor sport in area Record of visiting team Unobstructed view of game 80% of fans Per Season Basis Baseball & NFL: MlSL & NFL Baseball & NFL: MlSL & NFL: NHL & NFL Baseball & NFL Baseball & NFL Baseball & NFL Baseball & NFL: MlSL & NFL: NHL & NBA: NHL & NFL: Baseball & MlSL Baseball & NFL: Baseball & MlSL Baseball & NFL Baseball & NFL MlSL & NFL: MlSL & NHL MlSL & NFL: MlSL & NBA NHL & MISL: Baseball & MlSL MlSL & NFL Price of tickets for home game 3.03 p >.021 MlSL & NHL Weekend games 3.40 p >.014 MlSL & NFL Fourth quarter of season 3.55 p >.O11 MlSL & NBA: Baseball & NBA Team's involvement in 1st 2.63 p >.040 NFL & NHL place race First quarter of season 4.80 p >.002 Baseball & NFL Evening games 3.50 p >.012 Baseball & NFL It is noted that the items generating differences on a per game basis are cited in the literature as being mostly important to increasing attendance, yet there are differences between leagues, primarily between the NFL and baseball and between the NFL and other leagues. It could be that the NFL, having near sellouts in all of its franchise locations, is not concerned about these items; yet from the perspective of other leagues, particularly baseball, these are important to increasing attendance at games. League differences are evident on factor categories (see Table 6). Attendance items loaded on the highlighted categories of Table 6 are similar to those contained in Table 5, but any trends of leagues differing on the same attendance items and factor categories are not evident. This is due, as observed in Table 4, to different attendance items loading on different factor categories.

15 Factors Affecting Attendance Table 6 Analysis of Variance on Factor Categories Factor categories per game Factor categories per season 'p >.002 sig. diff. between MlSL & NFL, baseball & NFL; bp >.039 sig. diff. between baseball & NFL; 'p >,007 sig. diff. between baseball & NHL, baseball & NFL; *p >.027 sig. diff. between baseball & NFL; ep >.006 sig. diff. between baseball & NFL, baseball & NBA; 'p >.007 sig. diff. between CFL & NFL; MlSL & CFL; baseball & NFL; gp >.034 sig. diff. between MISL & NFL. Factor categories 1 and 5 present league differences for both game and season effect (Table 6); however, the items contained in each of these factors (Table 4) do not evidence any degree of similarity to create a trend. Therefore one is left with the fact that differences exist on factor categories involving the NFL in every instance. In the game effect of factor categories, it is clear that baseball differs from the NFL in Categories 1, 5, 6, and 9. Many items within these categories reflect a scheduling time of games (evening, quarters of season, etc.), quality of team rosters, price, and other entertainment competition. Although not significant, differences are noted for further study for Factor 3 per and Factor 2 per Given the popularity of the NFL in terms of sellouts and high fan interest over a shorter schedule compared to baseball's longer schedule, it could be concluded that baseball has more concern about these items and factor categories having an effect on attendance compared to the NFL. The same argument could be applied to the MISL and NHL in terms of Factors 1 and 4. This same rationale may be applicable to the season effect of factor categories also contained in Table 6. However, it is interesting to note the difference between the CFL and the NFL. One may conclude that differences in the marketing and competitive quality of Canadian and American football leagues accounts for this. Performance Level of Teams One would assume there would be differences between the relative performance of teams and factors affecting attendance, particularly between losing teams and winning teams. However, as indicated in Table 7, this is not evident. Given the emphasis placed on the quality of team rosters and overall play in terms of being a contender, only one item in Factor 1 (Table 4) is related to this rationale. Hence one may view the other items related to fan convenience as beifig as important for any team. Finally, the sample size and distribution of teams into the three categories of performance may explain much of the difference evident in Table 7 for both game and season effect of attendance.

16 Hansen and Gauthler Table 7 Differences Between Winning, Moderately Winning, and Losing Teams on Factor Categories Factor categories per game Factor categories per season Significant difference between winning and moderately winning teams: ap > 0.22; bp >,041 ; cp >.028. Indoor and Outdoor Leagues The rationale for classifying leagues as indoor and outdoor is based on facility size and weather conditions. Presumably, teams and leagues such as the NBA, NHL, and MISL would have a different perspective on items affecting attendance at their facilities with seating capacities of about 20,000 compared to the outdoor teams and leagues whose facilities have capacities approximating 70-80,000. The data did not substantiate this hypothesis for the game effect factors, and found the only significant difference for Factor 5 on the season effect basis (N=35; M=2.93; F=4.38, p>.044, with a significant difference between indoor and outdoor leagues). The items contained in Factor 5 (Table 4) are primarily economic, indicating that with smaller capacities the indoor leagues perhaps have a need for higher prices relative to outdoor leagues. Conclusions The literature review derived four categories of factors affecting attendance at professional sport events. The lack of data from personnel directly associated with professional teams, as well as the limited data from the six leagues in North America, was a stimulus for us to gain knowledge in this area. Questions that formed the basis for this study related to greater specificity of factors as compared to the literature, differences between leagues on each of the 40 items and the factor categories, and whether there were differences between indoor and outdoor leagues or between winning and losing teams. Hypotheses 1 and 2, dealing with league differences and attendance factors, were rejected, thus deriving a number of significant differences between three of the leagues. Baseball, the NFL, and the MISL accounted for these results on the factors affecting attendance. There were significant differences between leagues on attendance items such as scheduling of games in terms of weekends, afternoons, nights, and quarters of the season; the quality of team rosters as represented by star players; players attaining team or league records; ticket price;

17 Factors Affecting Attendance 31 the existence of other forms of entertainment in the area of the franchise; and such residual preference items as seating, cleanliness, and accessibility. The study derived 10 factors categories, each containiqg some of the 40 attendance items of the questionnaire. Hence, we feel that greater specificity has been gained compared to the four categories gleaned from the literature review. The response rate of 46% from a total of only 117 professional teams in North America accounts for the mixed results of the factor analysis. Although a few trends emerged relative to the attendance factors of team quality and the team being a contender in its division, conference, or league, as well as the scheduling aspects of the games, the remaining factors and items within them provide us with no definite trends. Enlarging the sample size to include Division I institutions of the NCAA, whose attendance at college basketball and football games rivals attendance at professional games, would provide sufficient data to attain specificity of factors and attendance items per factor by means of the factor analysis treatment of the data. The Latham and Stewart (1981) study on objectives of the 10 NFL teams regarding differences between winning teams, moderately winning teams, and losing teams was the basis for Hypothesis 3 of this study. Given a larger sample size, this hypothesis could derive more substantial weight. Offensive output of the team, an-item in Factor 1 (Table 4), is the only item related to team performance; the others focus on fan convenience and scheduling. In the per season effect, Factor 4 (Table 4) contains four items that relate to team performance in terms of record breaking and team output. Thus few results were evident in this study, but of the items cited (Table 7) one may assume that a larger sample size would provide more specificity regarding this question, especially between winning and losing teams. Finally, grouping the leagues by indoor and outdoor was based on attendance capacities of league facilities and weather conditions. Hence the assumption that factors affecting attendance at larger facilities of outdoor leagues would differ from smaller facilities of indoor leagues. The only factor of significance for the per season effect was Factor 5, which contained attendance items dealing with ticket price, accessibility, television coverage, and whether there were minor sport teams in the area. Although the evidence is not conclusive, smaller capacity indoor leagues have a need for higher ticket prices compared to larger capacity outdoor leagues. Demand studies of factors affecting attendance of North American professional sport leagues was found to be limiting in numbers as well as in substance. Data were lacking regarding some leagues such as indoor soccer and the CFL; in addition, there were no data from personnel directly involved in professional sport as to the relative importance of some 40 items affecting attendance. The clustering of attendance items into four areas (Table 1) found in the literature stimulated the need for factor analysis so as to derive greater specificity of items as well as clusters of factors affecting attendance. This study attempted to fill the void by providing a current status, specificity of attendance factors, derivation of factor clusters, and differences between leagues on various variables. Preliminary trends have been noted, and further research involving a larger sample of teams is needed in order to verify the trends found in this study.

18 32 Hansen and Gauthier Bird, P.J. (1982). The demand for league football. Applied Economics, 14, 637. Dawson, S.C., Malmisur, M.C., & Lewis, J.M. (1984). A comparative analysis of professional soccer in the United States and England. Journal of Sport Behavior, VE(3), 95. Demmert, N.G. (1973). fie economics of professional team sports. Lexington, MA: D.C. Heath. Drever, P., & MacDonald, J. (1981). Attendances at South Australian football games. International Review of Sport Sociology, 16(2), 103. Fillingham, E. J. (1977). Major league hockey: An industry study. Master's thesis, University of Alberta. Greenstein, T.N., & Marcum, J.P. (1981). Factors affecting attendance of major league baseball: Team performance. Review of Sport and Leisure, 6(2), 21. Hart, R.A., Nutton, J., & Sharot, T. (1975). A statistical analysis of association football attendances. Applied Statistics, 24(1), 17. Nay, R.D., & Thueson, N.C. (1986, October). High school attendance and related factors. Paper presented at Sport Administration Conference, University of Alberta. Hill, J.R., Madura, J., & Zuber, R.A. (1982). The short run demand for major league baseball. Atlantic Economic Journal, 10(2), 31. Jones, J.C.H. (1969, February). The economics of the N.N.L. Canadian Joumal of Economics, 2, 3. Jones, J.C.M. (1984). Winners, losers and hosers: Demand and survival in the National Hockey League. Atlantic Economic Journal, XII(3), 54. Latham, D.R., & Stewart, D.W. (1981). Organizational objectives and winning: An examination of the N.F.L. Academy of Management Journal, 24(1), 403. Medoff, M.N. (1976). On monopsonistic exploitation in professional baseball. Quarterly Review of Economics and Business, 16(2), 113. Nie, N.H., Hull, C.H., Jenkins, J.G., Steinbrenner, & Bent, D.H. (1975). Statistical package for the social sciences (2nd ed.). New York: McGraw-Hill. Noll, R.G. (1974). Government and the sports business (chap. 4). Washington: The Brookings Institute. Porter, P.K., & Scully, G.W. (1982). Measuring managerial efficiency: The case of baseball. Southern Economic Journal, 48(3), 642. Schofield, J.A. (1983). Performance and attendance at professional team sports. Journal of Sport Behavior, 6(4), 196. Scully, G.W. (1974). Pay and performance in major league baseball. American Economic Review, LXWr(6), 915. Siegfried, J.J., & Eisenberg, J.D. (1980). The demand for minor league baseball. Atlantic Economic Journal, 8, 59. Wilson, P.S. (1980, December). Winds up the willow. Melbourne, Australia. Zak, T.N., Huang, C.F., & Siegfried, J.J. (1979). Production efficiency: The case of professional basketball. Journal of Business, 52(3), 19. Zech, C.F. (1981). An empirical estimation of a production function: The case of major league baseball. American Economist, 25(2), 19. i 1 I?