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As Goes The Spoils, So Go The Victories: Exploring Major League Baseball’s Playoff Bonus System
### Abstract
This paper explores the playoff bonus system used by Major League Baseball. The unique incentive structure used is positively related to organizational success, and is studied using a Grounded Theory methodology. Exploration and analyses found that World Series winning teams prospectively distributed ten percent (10%) more shares than losing teams and repeat winners distributed still more. By developing an incentive structure as an intermediate step in the performance environment and linking the bonus’s value to future performance, decision makers may be able to positively impact organizational outcomes – particularly over repeated periods.
**Key words:** Major League Baseball, bonus system, payment, incentives
### Introduction
In October of 2007, the Colorado Rockies baseball team made the news for two reasons. First, they won 20 games in the last month of the season to wipe out a six game deficit and make the playoffs. The team became media favorites as announcers told and retold the story of how a franchise with one of the lowest payrolls in the game eventually became World Series champions. Moreover, reporters lauded the fact that success came not through the actions of one or two superstars, but by working together. This second reason for the media’s attention was the Rockies players’ decision to award a full playoff share to the family of Mike Coolbaugh.
Mike Coolbaugh was a minor league coach in the Rockies organization whose tragic and untimely death occurred on July 22, 2007 when he was struck by a foul ball during a Tulsa Drillers baseball game. At the time, he was working as a first base coach for the Drillers: a Double-A affiliate of the Colorado Rockies. The value of the playoff share eventually grew to $233,505.18 and earned the team a ‘Sportsman of the Year Award’ nomination from Sports Illustrated (38).
The purpose of this paper is to explore and explain the historical dynamics of the Major League Baseball’s (MLB’s) playoff bonus distribution system. That high performing teams’ members would be willing to sacrifice some of their own potential earnings to support their organizations’ staff members is, on the face of it, contrary to the ‘self-interests explanations’ (SIE; 14) commonly used in management, economic, and sociology theories’ literatures. Instead, it is more akin to a transformational leadership style commensurate with higher levels of organizational identification (30).
A Grounded Theory approach is used and a three-phase of the research methodology outlined by Schollhammer (43) – a relationship between share distribution and outcomes were hypothesized, an assessment of available data in the historic record was conducted, and the outcomes were evaluated based on current theories – are described. Both working managers and academic researchers can use the approach used herein to study the organizational phenomenon that commonly arise in Sport.
#### New Contribution
The paper makes two contributions managers can use to study and improve their organization’s performance. First, we present the MLB system as a case for exploring alternative frameworks for financial incentive structures. In particular, the roles of players’ decisions, rather than managers’ contributions (44), in influencing organizational success. Second, we analyze historic data, testing hypotheses developed using the grounded theory approach, to assess how patterns in share distribution practices are related to organizational performance outcomes.
For management researchers, MLB has offered a fertile empirical test bed. With the availability of data sets sometimes spanning over 100 years, baseball provides a natural experiment through which organizational behavior can be studied. In this case, we analyze the bonus system framework that can be explored in relationship to other more widely used approaches using controlled experimental designs that rely on the availability of data (44). Once completed, such research can provide decision-makers the ability to engage in a strategic process of setting appropriate performance indicators similar to those used in the “Moneyball” approach to player selection (49).
The background presented here follows an inductive/deductive processes used to study phenomenon where event timing is a critical element in the outcomes of interest. First, a brief description of the analytic strategy is given. Second, the historical context of MLB’s playoff bonus system is described. Next, data drawn from the MLB’s records is analyzed. Third, a theory is generated to explain the bonus system employed in baseball. Finally, we discuss the implications of timing in the development of incentive systems.
#### Background
Suddaby (48) both defined and outlined the use of Grounded Theory. Grounded Theory is as an approach positions phenomena as embedded in context that provides meaning that is often missing in traditional research. In other words, in the traditional model a researcher builds a hypothesis, constructs or identifies sources of data to test that hypothesis, and then tests that data to either support or reject the proposition developed. Suddaby argued that to do so ignores the perspective of the “actors” whose behaviors the data presupposes to study. Without exploring the intent of the actors, findings may be misrepresentations of the data. Further, to ignore the perspective of the actors is to ignore potential new theoretical lines of inquiry that emerge from their explanations.
The use of Grounded Theory in any context should then be defended as appropriate. Grounded Theory is most suited to efforts to understand interdependent processes, one where actors engage in an inter-subjective experience (48). The methodology is more effective when it considers extended time periods and when the relationships observed in the initial instance of the phenomenon are found to exist in other times or settings (16). Finally, the use of Grounded Theory is focused on how subjective experiences can be abstracted into theoretical statements about causal relations between actors.
Finally, it should be noted that individuals engaged in Grounded Theory research have an especial duty to explain the qualitative motivations in the research. As Grounded Theory should begin with a discussion of the data and then move to an exploration of the qualitative factors that directed inquiry, such an approach is often subjugated to the needs for traditional presentation forms for the sake of clarity – involving the presentation of a theoretical overview. In this paper, a three-step approach to exploring the phenomenon’s key elements and their relationships is employed, based on the strategies for sensemaking proposed by Langley (29). Collectively, the three phases of research used to understand the observed phenomenon – Describe, Analyze and Theorize – are referred to as the ‘DAT’ methodology. This method is particularly useful when data is temporally embedded in events rather than simple variables.
In the first phase, Define, a common narrative is developed from the process data based on unique instances of the phenomenon. The Define phase seeks to outline the context in sufficient detail from the actors’ perspective. Such an approach is requisite to assist in the narrowing of the theoretical frame for the study. Put another way, while many explanations are possible, they can be further limited by the context of the actors in situ.
The second phase, Analyze, focuses on the construction and execution of an analytic approach through which data on the dynamics identified in the Define phase are both gathered and processed in a manner that allows for simultaneous classification, to reduce complexity, and hypothesis driven statistical examination. These first two approaches are used to envelop and specify the phenomenon’s characteristics from both ends of the inductive research spectrum.
The final phase provides for the opportunity for Theory building based on an understanding of context and an assessment of data on the phenomenon being studied. Here, an alternative approach to sensemaking is used where different theoretical perspectives are applied in a deductive manner to determine how the phenomenon is best explained. The remainder of the paper follows the DAT format for investigating the incentive system used by MLB.
#### Phase 1: Describing the Phenomenon with Narrative
Groves argues that team dynamics are at play when “the decision makers base their decision choices on different information, yet are motivated by a common goal (19).” Further, Groves also notes that employee behavior may be more accurately analyzed in terms of the compensation they receive from the organizations for their participation in it. There are three features of the MLB Playoff bonus system that depart from most incentive and reward programs used by professional firms – the process of valuation, control and criteria.
Valuation addresses the resources set aside for the purposes of creating an incentive structure. In the broader business context, valuation is often a function of organizational history. The amount of resources set aside for the purpose of incentive distribution is not uniform as organizations specify the parameters on a case-by-case basis. Valuation can be understood not only in terms of how much, but also in terms of when the establishment of the specific value takes place. Further, valuation speaks not only to the pool of available resources for such incentives, but whether that pools is absolute or relative to firm performance.
For instance, an organization may have developed a practice of setting aside 10 percent of pre-tax profits for the purpose of distribution by management. For instance, an organization may decide that it sets aside 25 percent of any profit for distribution whereas another might only set aside that 25 percent if the organization exceeds last year’s profit levels. Additionally, organizations may simply allocate a fixed sum once a specific objective or subjective threshold is accomplished, for example, when the organization’s profits exceed 10 million dollars, one million is put into the profit sharing pool.
The second feature of bonus financial incentive systems is control. In this context, control speaks to the authority that allocates the bonus dollars. In most cases it is facilitated through the management structure. Control may be facilitated both directly and indirectly. Direct control is exercised when management evaluates individual performance to determine the degree to which an individual should be rewarded using the incentive pool. Indirect control is exercised when the organization codifies in contracts that specify the conditions under which an individual has a right to access the pool. For the most part, this process is considered a management responsibility.
Finally, the issue of criteria addresses the constraints and rules associated with participation in the pool. Organizations may, for instance, develop a pool for the sales staff. Such a pool is often aligned with compensation schemes to recognize high performers and represent constraints on the criteria for participation. Other organizations may create committees whose task it is to determine which employees meet the criteria for inclusion in the pool.
##### The Players’ Bonus in MLB
The bonus incentive model in MLB has an unusual scheme in that the system of valuation and control are far from the norm. Additionally, the criteria for distribution diverge from many bonus systems. To understand the formalization of this incentive structure, it is necessary to describe how the value of a share is calculated. We shall do so here in the order in which events happen so as to make specific points about the nature of what is both known and unknown at that point in the process.
##### Identifying the phenomenon’s key constructs
In 1903 the National Agreement united the American and National Leagues. This agreement created the World Series and stipulated that a portion of the playoff series gate receipts go directly to the participating teams’ players. At the time, prior to major radio or television contracts, the gate represented a significant portion of most teams’ total revenue. Therefore, the bonus system was a major revenue sharing scheme well ahead of its time (40). Further, the bonus system’s design and longevity are both rarities in the modern industrial era.
MLB has a playoff reward program and players on winning teams can earn substantial bonuses. Under the MLB playoff structure implemented in 1995 (the current system allows for three divisional champions and one ‘wild-card’ in each league), the players pool (P*) is comprised of 60 percent of the gate receipts from the first three rounds of the Division Series and 60 percent of the gate receipts from the first four rounds from the League Championship Series and World Series across the entire playoff contest. By only using the gate from the initial games, the formula presented in equation 1 does not incentivize the extension of the series. These receipts are divided as team shares among 12 clubs using a performance multiplier (MT) that escalates as the team progresses further in the playoffs, described in Table 1. The team share (PT) is then the fraction of the players’ pool that is available to divide among players.
**equation goes here**
This dynamic represents the first divergence from traditional bonus models. In MLB, the valuation process is prescribed but disjointed. Specifically, equation 1 specifies the value of the MLB players’ pool (P*), making it clear which teams are eligible for participation at the front, and allowing teams to estimate, based on historic trends the value associated with different performance outcomes. Individual team’s payouts escalate as they progress further in the playoffs with the World Series Champion getting the largest sum as prescribed by both Table 1 and equation 2. In 2006, the Players’ pool was $55.06 million and the St. Louis Cardinals took the lion’s share of $20.2 million (36 percent) by winning the World Series. That equates to $300,000 per player on the World Series winning team.5 (See Table 1)
At this point, a second divergence occurs from traditional models along the issue of control. In most cases the control of bonus dollars resides in management, however in the case of MLB, only players may allocate a portion of the players pool and they may do so in any manner they wish. This dynamic is significant in two ways. First, management has no ability to control the distribution of shares. Second, the distribution of shares occurs before the beginning of play in the post season. In other words, the employees create a system to allocate future performance-based earnings using a fractional system based on the concept of a “share” (Vs) as described in equation 3.
_Sporting News_ writer Todd Jones, a former MLB pitcher, notes that only players who have been on the roster the entire season are given a say in the allocation of shares. Players taken off the rosters for an extended period of time are generally not eligible to vote, and all players eligible to vote are all given a single share. The meeting then does not focus on the shares allocated to those in the room, rather it focuses on the shares to be allocated to those not in the room, and several players note that these meetings can become quite heated. Players often create “fractional shares” or simple “cash awards” (A) to recognize the work of auxiliary personnel. Because the allocation allotted to the team is fixed, every additional share authorized reduces the relative value of each player’s share. Additional discussion about the meaning of shares will be addressed in the next section. (See Table 2)
In summary, the MLB system of playoff shares provides a unique perspective into a system with a long history that is framed in such a way that empowers plays by giving them both control and allowing the players to determine the criteria. These decisions have an effect on valuation that is related to both performance and the share distribution decisions of the organization’s players. Each additional share distributed by the players reduces the value of the share each rostered player receives.
##### The meaning of a share
The authors were unable to secure interviews through the Major League Baseball Players Association. As a result, the need to explore what these shares meant to the players was critical to our DAT approach. Using Lexus-Nexus, the authors retrieved all news stories available on the service since 1980 using the search terms “playoff shares” and “baseball.” These 691 articles were then reviewed for the purpose of identifying what a share meant to the players. From those articles, duplicates were removed and articles outside of scope, as in the case of stories focusing solely on the value of the share as determined after the World Series or commentary provided by the writer. The remaining data was further segmented into direct quotes and evidence-based commentary whose origin was the players and their share deliberations.
Clearly players would forgo the value of a playoff share for the World Series title, but the underlying message was that the bonus is considered to be very significant and given in recognition of time served on the roster rather than performance of a specific player. For instance Astros General Manager Tim Purpura was quoted as saying, “The players that are on the roster as of June 1 typically get a full share… any players who were (on the roster) part of the season, they get voted on (by the players) (32).” A similar sentiment was made by the players of the Boston Red Sox who gave partial shares or cash rewards to anyone who wore the uniform, as well as a number of front office staff (24).
However, because the players make the decision according to their own preferences, time is no guarantee of a share. The players of the Toronto Blue Jays awarded former Chiefs outfielder Rob Ducey a small fraction of a share (16%), which was far less than others like Tom Lawless (59%), Rob MacDonald (25%), Mike Flanagan (20%) and Tom Quinlan (17%) even though Ducey was on the team longer than the other players (33).
Players have seen the exercise of their rights over the control of the playoff shares as a form of power. In 2002 the hitters in the San Diego Padres were so impressed by the work of one of the minor league coaches that they were able to “finagle” him onto the major league roster. He quit his job as a minor league coach but replaced it not only with a six-figure salary but a full playoff share after the Padres reached the World Series (7). Additionally, in 1996 the playoff shares were at times withheld from those crossed the picket lines. Mike Busch failed to receive a playoff share from the rostered players, despite a direct plea by then manager Tommy LaSorda. “What matters is the name on the front of your shirts, not the name on the back,” (17).
The dynamics inside these team meetings were noted as potentially contentious. Baltimore manager Johnny Oates, a former player, commented that he never liked team meetings but he did enjoy splitting up the playoff shares at the end of the year (22). A day after clinching the American League Western Division title, California Angels’s Rod Carew stormed out of a Sunday meeting in which the Angels decided how to divide playoff shares and World Series money among themselves. Carew would say only, “Money does strange things to people.” Carew, a native of Panama, apparently interpreted the decision to offer two Latino pitchers a half-share as discriminatory and angrily left the meeting. Reggie Jackson and Doug DeCinces were reported to have tried to restore the peace, but were unsuccessful (45).
Interestingly, the 1991 story of Dave Pavlas, Matt Howard and Dale Polley stood out in that it was the single instance where management, in this case George Steinbrenner of the New York Yankees, gave individuals each a check for $25,000 and a 1996 World Series ring, when the players voted to give them nothing. Steinbrenner also traded the primary force behind the decision to withhold the share, Jim Leyritz, the following day (4, 42).
Another ongoing commentary by the players is the financial value of a share for younger players. A few players identified the impact of a share on people who worked to create a winning team. In 1991 Norm Charlton of the Cincinnati Reds said, “You get two things for winning the World Series – a ring and our playoff shares.” He pointed out that for many of the younger players, the value of a share was the only recognition they received for winning a division or league championship. Further, he argued that the amount given was substantial relative to the salaries earned by some rookies (6).
Additionally, in organizations that traded players midseason, the players used shares to recognize these individuals’ contributions, even after having left to work for another competing organization. In 2004, the Boston Red Sox voted Nomar Garciaparra to receive a full share even though he was traded to the Chicago Cubs (18). When infielder Jeff Cirillo was released from the Minnesota Twins to the Arizona Diamondbacks, his final message was, “Good luck, and don’t forget about the old guy in the playoff share meeting” (34). This was again the case when the Florida Marlins players voted to give Pat Rapp of the San Francisco Giants a full playoff share for his contribution to the team before his trade (1).
In 2009, Major League Baseball (MLB) reported that the players for the Milwaukee Brewers offered former manager, Ned Yost, one of the Brewers 48 full postseason shares. The firing of Ned Yost marked the first time in major-league history — except the strike-split 1981 season — that a manager was fired in August or later with his team in playoff position. In their press release, MLB quoted an unnamed player who said “There are unwritten rules about how to do things, and it was the right thing to do. If you’re there more than 50 percent of the season, you’re pretty much getting a full share.” That same year, Nick Adenhart was killed in a car accident hours after starting as a pitcher for the Angels franchise. The Angels players voted to provide the late pitcher’s family a full share.
In summary, we found that players expressed a clear understanding of the diminishing return of the effect of the issuance of additional shares, that issues of equity, fairness and a sense of team spirit was a consistent theme, and that shares were also an expression of power by the players that could be exercised to make individuals feel either included or excluded. The series of events and decisions made by the Colorado Rockies described at the paper’s outset served as the impetus for the study at hand. However, it is clear that players use shares to recognize efforts of non-players as well as remediate perceived slights by management. While other teams have engaged in similar behaviors, none received the widespread coverage that the Rockies’ gesture did, in no small part because of their successful run to the World Series. The authors speculated that differences in share distribution patterns between teams might predict playoff series’ outcomes.
##### Shares and their relationship to outcomes
Emergent in this discussion is the idea that playoff shares can serve as an incentive for teams. Further, the structure involves an intricate timing that alters the manner in which the share itself is perceived. Table 1 speaks to the steps associated with the valuation, control and criteria used in share calculations. An initial component of evaluation is assigned in the securing of a place in the playoffs. At that point, team members know they are eligible for a share, however the specific value of that share remains unknown as it is based on future team performance. Rather than making the decision after the fact, the meeting that sets the criteria occurs place before any of the playoff series are played. This means that eligibility for a share is established before the value of that share in absolute terms is known. What is known is the value in relative terms.
The baseball system creates certainty around a payout whose value is unknown. While this may be a small point, it is a significant one. The implication of increased information provides short-term certainty other recognition systems often fail to provide. In 2008, 21 percent of all MLB players were paid less than $400,000, and the median payroll for a player was $1 million dollars. A playoff share will constitute a significant bonus for most of MLB players. For non-uniformed players, a share can triple or quadruple an annual salary.
The playoff shares system is distributed based on a criterion of past performance, where the control resides at the professional level to recognize peer performance, and creates a value that is based on future performance. Therefore, the major hypotheses are designed to explore share distribution and winning the World Series.
Hypothesis 1: Winners of the World Series will distribute significantly more playoff shares than teams that lose the World Series.
The expansion of the playoffs in 1995 provides a second period with potentially greater variability in the share distribution–playoff outcome relationship. With more teams in the playoffs over more rounds, the inference that distributing more shares is related to performance can be tested more rigorously. Therefore, the additional hypothesis is:
Hypothesis 2: Since 1995, Winners of the World Series will distribute significantly more shares than teams that lose the World Series.
It is posited that winning teams will distribute more shares. It stands to reason that players across the league will either implicitly or explicitly internalize this information. As a result, the number of shares distributed on average should increase over time. Therefore, another hypothesis arises:
Hypothesis 3: Baseball teams making the playoffs will distribute more shares over time.
It is possible to further explore the learning phenomenon by focusing on the teams that repeatedly make the playoffs. Having the firsthand experience assessing the playoff bonus – performance outcome relationship, teams that have won previously will seek to ensure their competitive advantage by increasing their distribution disproportionately. Therefore, the following hypothesis is made:
Hypothesis 4: World Series winning teams will distribute disproportionately more shares over time.
Based on this series of hypotheses, the Analytic Phase of the DAT process was undertaken.
### Methods
Baseball has been the subject of many empirical studies. One reason for the large number of studies is the availability of reliable and valid data on player demographics, performance outcomes, pay rates and incentives. Rottenberg (41) explored the market factors of the baseball labor market . A second reason for the interest in baseball’s statistics is the stability of the sport over time allowing players from various eras to be compared with some fidelity – the ‘dead ball’ and ‘steroid’ eras not withstanding.
With respect to compensation, Kahn (26) explored the relationship between managerial quality and salary. However, most of the studies to date have generally focused on baseball as a market phenomenon (5, 12, 13, 28, 35, 39). There have, to date, been few, if any, studies that have looked at the playoff shares incentive structure as described herein.
Using data publicly available on the number and value of World Series shares issued going back to 1903, the researchers tested the hypothesis that teams that were more egalitarian in the distribution of shares to non-uniformed players would be more successful. Because, within a year the size of teams should be relatively comparable, a simple different t-test was employed on the number of shares winning teams distributed prior to the start of the playoffs and the number of shares that the losing teams distributed. As only World Series shares were available from MLB, this hypothesis could only focus on winners and losers of the final series, and not on any of the championship and pennant races that allowed a team to compete in the final series. Based on the meaning behind shares, we argue that the differences within a single season can be attributed to shares allocated to players that played an incomplete season and auxiliary staff.
A second data gathering exercise focused on the playoffs that occurred after the introduction of the wild card in 1995. Wild card teams are ineligible for shares unless they win their division. Because this change to the system created new dynamics, we focused on the almost 30 years of data that emerged since that time. Again, data on shares were gathered from publically available data sources, including but not limited to MLB, _U.S. News and World Report_, and the Associated Press.
Analysis of the data was conducted using SPSS 17. Hypotheses 1-4 were tested using a t-test. This research involved the exclusive use of secondary data and as such was exempt research study per university institutional research board regulations.
### Results And Discussion
Phase 2: Analyses of the Available Data
Analyses of the relationship between World Series performance and playoff share distribution supports the assertion that winning teams are more egalitarian in their distribution of shares, authorizing more than their losing competition. Further, the dynamics of share distribution have been altered as the rules of the game have changed, most significantly with the initiation of free agency and the alteration to the playoff series’ format following expansions (23). Table 2 outlines the pattern of share distribution by franchise. (See Table 3)
Winning teams have historically been more generous (Hypotheses 1 is supported)
Over the 102 years of the World Series through 2006, we first calculated the difference in shares offered between winning and losing teams. Over that time period, winners distributed 88.93 more shares than losers, resulting in an average differential distribution of 0.872 shares for winning teams over losing teams (s = 3.65). The data supports the proposition that, within each year, winning teams offered more shares than losing teams (t = 2.407, p < 0.001). (See Figure 1)
Another way to look at the problem is to study the winner’s premium and compare it with the winner’s counter-premium. The winner’s premium is the number of shares issued by the winning team above what the losing team offered. For instance, when the winning and losing teams both issue 10 shares, an identical number of shares, neither is offered a premium. However, when the winning team issues 15 shares and the losing team issues 10 shares, the winner’s premium is said to be 5. The converse, the winner’s counter-premium exists when the winning team offers less shares that the losing team. The use of the term premium identifies the magnitude of the difference between winning teams that more distributive and those that are less relative to their competition. Figure 1 presents the winner’s premium/counter-premium in the form of a control chart where the y-axis is keyed to the standard deviation. Most statisticians would argue that the process only moves out of control in recent years, interestingly enough in a manner that coincides with the inauguration of the wild card rules of 1995. There is an additional anomaly that coincides with the expansion of MLB in 1965. (See Figure 2)
In the historic case, the winners’ premium has been 3.26 shares, based on 195.8 shares over 60 instances where the winner of the World Series is the team that has offered more shares. The winner’s counter premium has been 2.89, based on 102.2 shares over 38 instances where the winner of the World Series is the team that has offered more shares. Further, winners were 1.6 times likelier to have offered more shares than losers. When more generous teams won, they generally authorized 3.26 shares more than their losing competition. When the less generous team won, the difference between the winners and losers was actually smaller (2.7 shares).
Additionally, it should be noted that repeated appearance in the playoffs is related to salary. An analysis of shares authorized correlates to the payroll of the team (r2 = 0.26). However, it should be noted that making it to the playoffs multiple times does not necessarily correlate with higher salaries. A similar analysis was conducted by adding the frequency the team appeared in the playoffs for that same time period. In that case, the quality of the correlation dropped (r2adj=0.22).
Therefore, a minimum share distribution spread may exist before differential performance is realized. Therefore, we find that Hypothesis 1 is supported in both analyses.
Additionally, we focused on the distribution parameters since the baseball playoff series was restructured in 1995 through the most recent data available in 2008. The results in this case were not significant. Over that time period, winners distributed 0.74 more shares than losers, resulting in a negligible differential distribution of 0.05 shares for winning teams over losing teams (s = 5.53). In those same years, the winning team offered more shares as often as the losing team – six of twelve times the World Series were won by the organization offering the winner’s premium. However, further analysis of the data shows that the winner’s premium of these years was 6.05 and the winner’s counter-premium was 4.33. This provides some evidence that effect is relatively weak and that limiting the time to twelve years may have undermined the power of the analysis. Therefore, while Hypothesis 2 is not supported, there is counter evidence that may need further study.
Overall, in the historical model, winners were 1.6 times likelier to have offered more shares than losers. Further, when more generous teams won, they generally authorized 3.26 shares more than their losing competition. When the less generous team won, the difference between the winners and losers was actually smaller (2.7 shares). Therefore, a minimum share distribution spread may exist before differential performance is realized. This analysis limited to the World Series between the years of 1995 to 2008 found winners were as likely to have offered more shares than losers. Further, when more generous teams won, they generally authorized 6.05 shares more than their losing competition. When the less generous team won, the difference between the winners and losers was actually smaller (4.33 shares).
Teams have become more generous over time (Hypothesis 3 is supported). There has been a general trend to increase the number of playoff shares. On an annual basis, teams in the World Series have increased the average number of shares distributed annually by 0.31 (r2 = 0.78). Since 1995, the average number of shares authorized by teams in the World Series has increased 1.03 shares annually, with an Adjusted R-squared of 0.330. When that analysis is expanded to include the divisional champions for both the American and National League, the distributional structure does not change (1.10 v. 1.03 shares annually), but the explanatory factor is increased significantly (Adjusted R-squared = 0.61), suggesting that the increase is foundational and the diminished explanatory power can be attributed to the relatively small data set in the subset analysis. Therefore, Hypothesis 3 is supported in both the full sample and the period from 1995 to 2008.
Repeat winners are still more generous (Hypothesis 4 is supported). Among World Series winning teams, there has been a consistent increase in the number of shares distributed on average. For every additional World Series a team has won in the past 1995 to 2008 period, teams offered an additional half-share (r2 = 0.32). Winning teams have developed a winning formula with respect to share distribution. Further, that players learn to forgo the initial temptation for engaging in SIE behaviors at the outset is important, but not predicted in most of the theories employed by management, economic and sociology researchers. Therefore, it would be beneficial to have a set of guiding principles prior to implementing such an incentive system; hence, the need for a theoretic exploration of MLB’s bonus system.
#### Phase 3: Theoretic Assessment of MLB’s Bonus System
The narrative and empirical evidence offer a new opportunity to explore the theoretic paradigms used to explain and create bonus systems that promote organizational missions. Issues of power, equity and pro-social behavior, and performance forecasting characterize playoff share distribution decisions and their positive impact on organizational outcomes. They also constitute decision-making in an environment where the motivation can range from purely self-serving (e.g., Self-Interested Economics (SIE) explanations) to completely egalitarian. With respect to SIE motivations, Principal-Agency is the most widely used model for exploring compensation issues in organizations – Principal-Agency Theory (8). At the other end of the spectrum is Equity Theory that predicts the remuneration system will distribute shares across all participants at equal levels. The range of theories is explored from most self-interested to most egalitarian.
##### Alternative theory one – principal-agent theory
Bonus systems are often used as an extension of governance policies to promote congruence between principals’ and agents’ goals. Therefore, the topic of incentive-reward systems often arises in the economics’ literature as a search of Google Scholar using the term “bonus system theory” reveals. In particular, economics views such systems through the lens of contractual arrangements where an agent’s rationale behavior is to maximize their utility by fulfilling the contract and doing nothing more (50).
The limits and boundaries of agency theory lie in its model of human motivation. In the case of baseball, economic models based on the ‘rational’ or ‘economic man’ would suggest that the optimum distribution for the individuals (agents) acting on their own behalf would be to distribute a maximum of 40 full shares (47). Given that few if any teams go a full season without roster changes, the number of full share equivalents distributed should be less than 40 with only fractions given to individuals who have been on the roster for partial period.
The near-term financial incentives are for the empowered players to act in their own interest without consideration for non-uniformed stakeholders or players no longer on the roster. The reality is far from this. In 2006, players on each of the teams in the playoffs authorized the creation of 52.86 shares, on average. Therefore, the prima fascia evidence indicates that one of Agency Theory’s main tenets is violated by the bonus system used by MLB. Beyond the shortcomings of economic theories identified herein, other researchers have suggested that other organizational theories may have greater explanatory power (2, 50). In particular, they point to Stewardship Theory for addressing the egalitarian features of the bonus system used by MLB instead of Agency Theory (9, 10, 15, 50).
##### Alternative theory two – stewardship theory
Stewardship Theory describes a phenomenon where an agent’s goals are subordinated to a broader organizational or societal aims. Stewardship Theory assumes an individual’s decision model is ordered such that pro-organizational, collectivistic behaviors have higher utility functions than individualistic, self-serving behaviors. A steward protects and maximizes the organization’s collective wealth through superior firm performance, because, by so doing, the steward’s utility functions are maximized. Alternatively, the steward may calculate that working toward organizational, collective ends is the best trade-off between personal needs and organizational objectives. Hence, the utility gained from pro-organizational behavior is higher than the utility that can be gained through individualistic, self-serving behavior.
The main problem with Stewardship Theory, as it applies to MLB’s bonus system, is that incentive pay is antithetical to the primary model of behavior anticipated. From a Stewardship Theory perspective, how bonuses are used or distributed within organizations would have little influence on employees’ individual motivations to work hard toward organizational goals. Given players are earning a living wage, which is assured with the minimum pay agreed to through the Collective Bargaining Agreement, no other motivation than those intrinsic to the organization’s aims should be needed.
There is also objective evidence that Stewardship Theory is not applicable to MLB. In addition to the 1919 Black Sox scandal, a reference to game fixing by the Chicago White Sox, the widespread use of steroids more recently provide both specific and general examples that players are not fully vested stewards protecting the integrity of the game’s values and traditions. With respect to the latter, steroid use, not only have players cheated and skewed the on-field records, they have also lied repeatedly to cover their misdeeds, and have engaged in this behavior in what seems to be a collective mind rather than individual exception (37). The players were able to engage in this behavior for an extended period of time under the protection of their union’s collective bargaining agreement (46). While steroid use was widespread, and alleged to have been endemic to some teams, the difference between individual versus collective decisions may be explained using another pair of theories.
##### Alternative theory three – expectancy and equity theories
Psychological paradigms such as Expectancy and Equity Theories have been applied to organizational compensation questions and baseball in particular. These theories explore the positive versus negative aspects of individuals’ behaviors under pay-for-performance situations. There has been significant work in the area of equity and expectancy theories in baseball. Lord and Hohenfeld (31), Duchon and Jago (11), Hauenstein and Lord (21), and Harder (20) studied the performance of baseball players who were either free agents or had been through salary arbitration. This line of research has looked at the pay-for-performance question based on salaries, not collectively distributed bonuses. Further, a major reason for implementing a bonus system is to avoid the managerial transaction costs of micro-managing employees’ individual expectations and trying to equitably fulfill them. Indeed this is a major benefit of creating an incentive system controlled directly by the employees.
The teams’ distributions of playoff shares and performance outcomes allow for some inferences to be made with respect to Expectancy and Equity. In particular, the distribution of more shares could be equated to greater perceived equity, which in turn leads to improved performance. However, the distribution of more shares could be defined as egalitarian without any regard to merit with equal veracity. Therefore, expectancy and equity theory are problematic under the given circumstance for a manager wishing to take advantage of a program with a proven record of success in promoting organizational goals.
There are other theories that can be applied to MLB’s bonus system and other potential propositions arise from the results herein. Institutional Theory could be used to explain the increasing amount of shares being created as teams engage in a mimetic process based on the winners’ behaviors. Alternatively, human resources researchers might propose that there will be greater player retention among more egalitarian squads. Both of these lines of inquiry will require additional information that is best gained by going directly into the process.
### Conclusion
MLB’s playoff bonus system exhibits many desirable characteristics, but it does not conform to the most commonly used found in the design of such systems. It is important to reiterate the unusual features of the system. Shares have nominal value at the point they are issued, and gain in value as a team progresses through the playoff series by performing competitively. Allocations generally include some aspect of retrospective acknowledgement – shares are allocated to the people that the core players believe have worked hard to get their team to the playoffs, paid against a value defined by future performance. These people can be players, support staff, or managers. As a result, beneficiaries of shares have full information about the distribution of shares without knowledge of the value of those shares. However, because these shares are allocated prospectively, share recipients can alter behaviors in ways that maximize the value of those shares. In this respect, the system constitutes an incentive structure on a pay-for-performance basis.
The link between performance and reward distribution is one mechanism that management scholars have long advocated as an important motivational or demotivational tool (25). In particular, the use of group incentive plans are recommended when the group is small, team members are engaged in the same kind of work, the payment is clearly linked to the performance, the output depends on the workers, the operational cycle is fairly short, the bonus is substantial relative to standard pay, and there is an atmosphere of mutual trust between management and workers (3, 36). The MLB playoff bonus system is such an incentive plan and has been in existence since 1903. Further, it has an unusual feature in that the players determine playoff bonus share distribution, not only amongst themselves, but also to managers, trainers and other staff members in the organization.
The data from this analysis supports a bonus system that creates incentives for individuals that linked performance to outcomes in ways determined by the principal actors, but not actively managed by owners may support performance in substantive ways. Further, to the extent that such efforts are seen as egalitarian and recognizes the broader contributions of individuals on the margin, such distributions seem to support overall organizational success. Additionally, and maybe more significantly, this system defines the reward structures prospectively. While most players would consider winning the World Series the ideal outcome, this system creates process incentives in addition to outcome incentives. This represents a significantly different method for incentivizing outcomes. Most bonus systems are designed and allocated after the fact with little to no information given to the recipients of the reward.
MLB players have elected to distribute progressively more playoff bonus shares as time progresses. Therefore, there is some form of institutional knowledge accruing. Further, that teams with more experience distribute still more shares than their competitors is an indication that the feedback mechanism functions in a positive manner (27). Collectively, the hypotheses demonstrate the efficacy of MLB’s bonus system.
### Applications In Sport
The distribution of annual bonuses by professionals to themselves and other organizational members is a common feature of law practices, investment banking firms, consulting practices and some other closely held businesses. Bass suggests that group incentive plans are recommended under specific instances. In practice, these distributions are typically allocated retrospectively when the bonus pool’s amount is known using performance standards determined ex post facto. Generally, the professionals (e.g., partners) determining the distribution are the firm’s management.
Studying the distribution of World Series playoff shares provides a unique glimpse into an incentive scheme. The MLB Playoff Shares system uses a valuation system where non-managerial professionals determined share allocation a priori and the bonus amount is not assured, and the performance measure – winning the World Series – is absolute. The baseball model is a system where a bonus is structurally defined, controlled by the agents of the organization with the greatest direct ability to impact outcomes, and is retrospectively allocated but prospectively incentivizing. The incentive structure created is therefore both prospective and retrospective, and moreover, the timeline presents a short horizon to gain, addressing the common pool resource problem of long-term versus short-term feedback. Therefore, if the incentive scheme is an efficacious one, it may warrant broader adoption amongst other professionally led organizations seeking to improve their performance.
### Tables
#### Table 1
Performance Multiplier
| Title | Number | Performance Multiplier |
|---|---|---|
| World Series Champion | 1 | 0.36 |
| League Champion (World Series Loser) | 1 | 0.24 |
| League Champion Series Runners Up | 2 | 0.12 |
| Division Series Runners Up | 4 | 0.03 |
| Second Place Finishers (Non-Wild Card Clubs) | 4 | 0.01 |
#### Table 2
MLB’s Bonus System’s Design
| Construct | Definition |
|---|---|
| Between Team Pool Determination | Formula-driven and invariant from year-to-year. System rewards teams, not individuals. Team’s payout varies based on how they finish in the playoffs. |
| Allocation Timing | Share distribution is determined prior to share valuation. |
| Within Team (Work Group) Control | Front-line employees – players – determine their own and staff members’ shares in an informal process designed to incentivize team aims. |
| Allocation Criteria | Measurement models unknown. Only players that were on the roster the entire season have control of the bonus allocation. Rosters are expanded for the post season; therefore, some aspect of tenure likely plays a role. In addition, the large wage disparity between ‘star’ players and other organizational members may result in a premium on altruistic distribution schemes. |
| Valuation | A share’s realized value is not known until the team’s season ends. Teams that perform better vis-à-vis other units receive progressively greater rewards. |
#### Table 3
Share distribution by winning franchise, sorted by Average Share Premium (1903 to 2008)
| World Series Championship Franchise | Number of Wins | Average Share Premium |
|---|---|---|
| Chicago White Sox | 3 | -5.83 |
| Florida Marlins | 2 | -5.55 |
| Minnesota Twins | 2 | -2.00 |
| Atlanta/Milwaukee/Boston Braves | 3 | -1.87 |
| Cleveland Indians | 2 | -1.20 |
| Toronto Blue Jays | 2 | -1.10 |
| St. Louis Cardinals | 10 | -0.88 |
| Washington Nationals | 1 | -0.80 |
| SF/NY Giants | 5 | -0.44 |
| LA/Brooklyn Dodgers | 6 | 0.37 |
| Detroit Tigers | 4 | 0.70 |
| Oakland/Philadelphia Athletics | 9 | 0.76 |
| Anaheim Angels | 1 | 1.20 |
| Chicago Cubs | 2 | 1.20 |
| Cincinnati Reds | 5 | 1.40 |
| Pittsburgh Pirates | 5 | 1.50 |
| Baltimore Orioles | 3 | 1.53 |
| Boston Red Sox | 5 | 1.95 |
| Kansas City Royals | 1 | 2.70 |
| New York Yankees | 26 | 2.73 |
| Arizona Diamondbacks | 1 | 3.00 |
| Boston Americans | 1 | 4.00 |
| Philadelphia Phillies | 1 | 4.45 |
| New York Mets | 2 | 6.20 |
### Figures
#### Figure 1
Premium/Counter-premium Control Chart (World Series: 1903 – 2008, y-units are 1)
”)
#### Figure 2
Share distribution of Playoff Teams by Frequency of Participation (1995 through 2008)
”)
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### Corresponding Author
Eric W. Ford, MPH, Ph.D.
Forsyth Medical Center Distinguished Professor of Management
The University of North Carolina Greensboro
PO Box 26165
Greensboro, NC 27402
<ewford@uncg.edu>
Phone: 806-787-3267
Fax: 336-334-5580
Upon Further Review: An Empirical Investigation of Voter Bias in the Coaches’ Poll in College Football
### Abstract
#### Purpose
The popularity of NCAA football continues to rise at an exponential rate. As revenues increase, the difference between a BCS bowl berth and a non-BCS bowl berth can be millions of dollars. Thus, the process of how schools are selected to play in a BCS bowl game is very important. In this paper, we analyze one of the components of the BCS ranking system: the Coaches’ Poll.
#### Methods
Data from the final regular season Coaches’ Poll from 2005 through 2010 were analyzed in order to explore whether coaches were biased in their voting in three different areas: voting for their own team, voting for teams in their conference and voting for teams from Non-Automatically Qualifying (N-AQ) conferences.
#### Results
Through analyzing a Coach’s Difference Score (CDS), we found that coaches had a positive bias towards their own team. That is, they vote their own team higher than their peers. We also discovered that coaches tend to vote schools from their own conference higher than do coaches from outside that conference. Finally, we concluded that coaches from the six Automatically Qualifying (AQ) conferences were biased against schools from the smaller N-AQ conferences.
#### Conclusions
After discussing potential reasons why all these biases occur, several questions for future researchers to explore are put forth. Then, we make several suggestions to improve the voting process in order to make it as objective as possible.
**Key Words:** College Football Coaches’ Poll, Voting Bias, BCS Implications
### Introduction
Every year in college football, a debate occurs about which team should be ranked higher than another, and 2010 was no different. With three teams finishing their regular seasons undefeated, it was up to the Bowl Championship Series (BCS) rankings to determine which two teams would play for the national championship. While Auburn defeated Oregon in Glendale, Arizona, on January 10, 2011, and was crowned champion, fans over a thousand miles away in Fort Worth, Texas were left to wonder, “Could TCU have beaten Auburn?” Thus, the scrutiny of the BCS continues.
The BCS system was started in 1998 as a way to bring the top-two ranked teams face to face in a bowl game to determine a national champion (3). Prior to the BCS, the bowls tried to match number one versus number two, but with guaranteed conference tie-ins, such as that of the Pac 10 and the Big 10 to the Rose Bowl, it was not always possible. When the Rose Bowl relented, the BCS was born. According to the official BCS website, “The BCS is managed by the commissioners of the 11 NCAA Football Bowl Subdivision (“FBS”) (formerly Division I-A) conferences, the director of athletics at the University of Notre Dame, and representatives of the bowl organizations. The conferences are the Atlantic Coast, Big East, Big Ten, Big 12, Conference USA, Mid-American, Mountain West, Sun Belt, Pacific-10, Southeastern and Western Athletic” (3).
As of 2005, the BCS standings are determined by averaging three different rankings: the Harris Poll, computer rankings and the Coaches’ Poll. The Harris Poll is run by a marketing research firm, Harris Interactive, and is “comprised of 114 former college football players, coaches, administrators and current and former members of the media…randomly selected from among more than 300 nominations” (10) from the FBS. The final computer ranking used is an average of the rankings from six different firms/individuals that mathematically calculate a team’s ranking based on wins, strength of schedule, etc. (3). The Coaches’ Poll is run by *USA Today* and the American Football Coaches Association (AFCA) and is approximately 60 coaches–50% of the coaches in each conference are randomly selected to vote (18).
This research explores one component of the BCS: the Coaches’ Poll. In particular, we investigate to what extent coaches have been biased in their voting. Bias, as defined herein, is considered to be present when a coach ranks a team significantly different than the other voting coaches in the poll. Why is this important? With teams often being separated by a few tenths of a point in the BCS standings, ensuring the integrity of the rankings is critical. The BCS standings can determine a team’s bowl game and/or a coach’s bonus. For example, Iowa coach Kirk Ferentz received a $225,000 bonus for finishing in the Top 10 BCS rankings in 2009, and another $175,000 bonus for playing in the BCS Orange Bowl that season (8). In 2010, the BCS bowl payout was 17 million dollars (6) with the non-BCS bowl payout being much less (e.g., The 2010 Capital One Bowl had the highest non-BCS Bowl payout of 4.25 million dollars (6)). So, biased decisions may not only affect the coaches, who make these decisions, but other coaches and universities, as well.
Prior to 2005, the coaches’ votes were not made public. Then, in response to added pressure for transparency, a vote by all FBS coaches made the final regular season Coaches’ Poll public by agreeing to have the ballots published in *USA Today* (7). However, the decision was not unanimous. According to Texas coach Mack Brown, who was initially not in favor of making the votes public, “It can put coaches in a difficult situation” (7). How did the first year of public voting go? According to Sports Illustrated writer, Stewart Mandel, it was “the equivalent of a high school student-council election” with “Oregon coach Mike Bellotti, his team about to be squeezed out of the BCS by Notre Dame, placing the Ducks fourth and the Irish ninth,” and “Arkansas coach Houston Nutt ranking SEC rival Auburn third and Big East champion West Virginia … nowhere.” (14). Even Coach Steve Spurrier of the University of South Carolina has questioned the validity of the Coaches’ Poll remarking, “I guess we vote ’cause college football is still without a playoff system. I really believe most coaches do not know a whole lot about the other teams” (9).
With increasing scrutiny of the coaches’ voting patterns, the AFCA hired The Gallup Organization in early 2009 to analyze the coaches’ voting and make recommendations. “The perception is that there’s a huge bias, and we’ve never really found that,” claimed former Baylor coach and current AFCA Director Grant Teaff (2). One of Gallup’s key recommendations was to make the coaches’ final regular season votes private. However, after seeing the response to a *USA Today* poll of over 4,000 readers that found 79% of fans felt the coaches’ final regular season votes should remain public as “it is important they are accountable,” (20) the AFCA put the decision to a vote of all FBS head coaches, and the results indicated that the final regular season votes should remain transparent. Consequently, the AFCA changed their mind and kept the final regular season votes public in 2010 (1).
Even with the continued visibility of the voting, one thing remained consistent in 2010: scrutiny. For example, in the final vote of 2010, one coach returned his ballot with TCU ranked number one, which is against the AFCA rules (the AFCA instructs every coach to list the winner of the BCS National Championship Game as the top ranked team) and two other coaches in the poll failed to turn in their ballots at all (18). While the final votes of each coach are not made public, these types of mishaps still fuel the debate: Should coaches have a part in the BCS rankings?
Previous researchers have discovered that individuals can be biased towards others in society (11, 12), and that people can also be biased when voting (16, 17). Specifically, researchers have examined voter bias in college football polls. For instance, Coleman et al. (5) concluded that voters in the 2007 Associated Press college football poll were biased in a number of different ways, including voter bias toward teams in their home state. In another study, Campbell et al. (4) discovered that “the more often a team is televised, relative to the total number of own- and opponent-televised games, the greater the change in the number of AP votes that team receives,” (p. 426) when they analyzed the AP votes from the 2003 and 2004 college football seasons. A study by Paul et al. (15) also examined AP voting bias, but included coaches’ voting as well. Their research looked at both of these polls from the 2003 season, and they determined that the spread or betting line on a game is “shown to have a positive and highly significant effect on votes in both polls. A team that covers the point spread will receive an increase in votes in both polls. A team that wins, but does not cover the point spread, will lose votes” (15, p. 412). In 2010, Witte and Mirabile (21) extended the literature by examining several seasons of Coaches’ Poll data, and they concluded that voters tended to “over-assess teams who play in certain Bowl Championship Series (BCS) conferences relative to non-BCS conferences” (p. 443).
While research on the voting bias in the college football polls exists, few researchers have investigated the bias in the Coaches’ Poll to any great depth. Hence the purpose of this research is to determine if college football coaches are biased when they vote and, if they are, what kind of biases they hold. Specifically, we look at three areas of potential bias put forth in the following null and alternate hypotheses:
> H1o: Own-School Bias – Coaches do NOT rank their own teams significantly different than other coaches voting.
>
> H1a: Own-School Bias – Coaches do rank their own teams significantly different than the other coaches voting.
>
> H2o: Own-Conference Bias – Coaches do NOT rank teams within their conference significantly different than coaches from outside the conference vote those same teams.
>
> H2a: Own-Conference Bias – Coaches do rank teams within their conference significantly different than coaches from outside the conference vote those same teams.
>
> H3o: N-AQ Bias – Coaches from schools in the AQ conferences do NOT rank N-AQ teams significantly different than the N-AQ coaches voting.
>
> H3a: N-AQ Bias – Coaches from schools in the AQ conferences rank N-AQ teams significantly different than the N-AQ coaches do.
Combining the three hypotheses, a model for Coaches’ Voting Bias is shown in Figure 1.
The first hypothesis investigates whether coaches can be objective when ranking their own teams. Do coaches rank their own teams about the same as other coaches rank that team, or, do coaches tend to over-estimate their own team’s ranking? The second hypothesis explores whether coaches rank teams in their own conference impartially. Many times a team’s quality of wins and losses can impact the perception of how good they are, and if a coach makes teams in their conference look superior to those from other conferences, perceptions of the strength of their own team may increase. Our final hypothesis examines what is commonly called “big school bias.” Namely, do coaches from the six traditional power conferences that have automatic qualification tie-ins with the BCS (AQ teams) tend to underestimate the strength of teams from the smaller conferences, the winners of which do not automatically qualify for a BCS bowl (N-AQ teams)?
### Methodology
The sample for this research was the final regular season coaches’ ballots for the 2005 through the 2010 college football seasons published in the *USA Today Coaches’ Poll*. In each of these years, a coach who is selected to vote ranks his top 25 teams by awarding 25 points to his top ranked team, 24 points to his second ranked team and decreasing in a similar manner until the 25th ranked team is awarded a single point. Appendix A lists the various coaches and the years during which each has been a member of the *USA Today Coaches’ Poll*. Table 1 aggregates the data by conference.
Because the number of coaches who vote each year varies slightly, a simple linear transformation of the total point system was employed herein by calculating a voter’s “difference score,” which is the average number of points a team received subtracted from the points that the voter awarded them. For instance, in 2008, the #20 Northwestern Wildcats received a total of 334 points and 61 coaches voted in that poll. So, Northwestern’s average points per coach is calculated as 334/61 = 5.475. In that poll, Coach Bret Bielema of Wisconsin gave Northwestern 8 points. Thus, his difference score would be 8–5.475 = 2.525. On the other hand, Coach Art Briles of Houston gave Northwestern only 4 points that year resulting in a difference score of 4 – 5.475 = -1.475.
In general, a positive difference score suggests that a coach ranked a team higher than that team’s average score, while a negative difference score indicates that a coach ranked a team lower than its average score. A small difference score represents a case where a coach has ranked a team very close to the average ranking of his peers. In contrast, a large difference score would suggest a coach disagreed with his peers about where a team should be ranked. The total of the 25 difference scores for each individual coach will sum out to zero each year as every time a coach votes a team higher than his peers, he must vote another team (or a combination of teams) lower than his peers. Likewise, when all the coaches’ difference scores for a single team are summed, there will be a difference score of zero (i.e., for every coach that votes a team higher than their final average, there must be a coach, or combination of coaches, that votes that team lower). Thus, the key unit of analysis in this study is a term we have labeled the Coach’s Difference Score or CDS.
One thing to note about the Coaches’ Poll is just how few votes can separate teams. Roughly seventeen percent of the point differences between two contiguous positions in the poll were determined by fifteen or fewer points. In fact, in fifteen occurrences, which is an average of 2.5 per year, less than six total points separated two teams, including in 2008 where a single point separated #1 Oklahoma (1,482 points) and #2 Florida (1,481 points).
### Results
In order to test Hypothesis 1, which explores whether there are any discernible patterns in how coaches rank their own schools, we employed a simple t-test. If there are no significant biases (as a collection), coaches that vote on their own teams will have a mean CDS score of zero (i.e., for every coach that ranks his team higher than his peers, a corresponding coach would rank his team lower than his peers). However, if coaches tend to error consistently on one side or the other from their peers, then the difference score for those coaches will not be equal to zero. We tested each of the six years individually and collectively. The results are summarized below in Table 2.
As illustrated in Table 2, in all six years, the CDS, which ranged from a low of 1.61 (in 2008) to a high of 3.12 (in 2007), were significantly different than zero at a p < .01 level. This result leads us to reject the null hypothesis that there is no bias in the way coaches vote their own school. The result indicates that coaches do tend to rank their own teams significantly higher than do other coaches. Over the entire sample, coaches, on average, ranked their own team 2.32 positions higher than did their peers. We explored this result further by performing an ANOVA test across the six periods to see if any one year’s bias was significantly higher or lower than the other years. The ANOVA result was not significant (F = 1.083, p = .373) leading us to conclude that there is no statistical evidence to suggest that the bias changes from one year to the next. While this result might seem trivial on the surface, it tells us that no matter how much the composition of the voting group changes (e.g., only eight of the sixty-two coaches that voted in 2005 were still voting in 2010), the coaches vote in a fairly homogeneous way when it comes to ranking their own teams.
In order to test Hypothesis 2 for within conference bias, we assessed the CDS for each voting coach, with regard to their respective conference members. To control for own-school bias, we did not include a coach’s own team in the analysis. We tested for this own-conference bias for individual seasons, as well as, collectively, for the entire span of years examined. We employed the same set-up and methodology (i.e., a t-test) that we used to test H1. Table 3 reveals the results of these t-tests.
All of the t-test results were statistically significant (p < .001) leading us to reject the null hypothesis. This result suggests that coaches do rank their own conference members higher than do coaches outside the conference. While the CDS overall mean of 1.19 might seem small, keep in mind that some conferences have as many as seven voting members, and others as few as three, which could lead to an average favorable bias of nearly 5 points (1.19 * (7 – 3)) for the teams in a conference with seven voting members.
To explore this bias further, we conducted two additional ANOVA tests. The first to discover whether the mean CDS had changed across the six years and the second to determine if any one conference’s coaches have a higher CDS than those of other conferences. With an F-statistic of 4.286, the first ANOVA test was significant at the p < .001 level. A post-hoc Tukey test (p < .05) indicated that own-conference bias was significantly higher in 2007 (with a mean of 1.95), than in each of the other years. This result might be explained by noting that, in 2007, there were only two teams from the traditional AQ conferences, Ohio State and Kansas, which had one loss or less, while a total of ten teams had two losses, potentially making it very difficult to sort out what schools rounded out the Top 10. As a result, coaches ranked the schools with which they were familiar (their own-conference schools) higher than other schools. That year was the only one within our sample range that had such a grouping of teams with similar records. Sixteen different teams received top 10 votes that year, which was the largest amount for any year.
We then turned our attention to analyzing own-conference bias broken down by conference membership. Table 4 gives the descriptive statistics for each conference. The ANOVA analysis suggests that we cannot accept the null hypothesis of equal bias across conferences (F = 4.286, p < .001). Post-hoc Tukey comparisons reveal that the own-conference bias of voters from the WAC was significantly greater than the bias from coaches in the ACC, Big 10, Big 12, C-USA, MAC, Pac 10 and SEC (p < .01). The primary beneficiaries of this effect were Hawaii in 2007, with the four WAC coaches voting Hawaii an average of 5.27 positions higher than non-WAC coaches, and Nevada in 2010, with the four WAC coaches voting Nevada an average of 5.4 positions higher than the non-WAC coaches.
Our final hypothesis, H3, investigates whether bias exists in the way coaches from AQ conferences, whose champions automatically qualify for a BCS spot, vote versus the way coaches from conferences that do not have automatic tie-ins, referred to as N-AQ conferences, vote. The six AQ conferences include: ACC, Big 10, Big 12, Big East, SEC and Pac 10. The remaining five conferences are categorized as N-AQ: C-USA, MAC, MWC, Sun-Belt and WAC. Ultimately, we are testing what many journalists call “big school bias”–whether or not coaches from the six AQ conferences are biased against the smaller N-AQ schools. To test for this bias, we assessed how coaches from the AQ schools ranked the N-AQ schools, compared to how coaches from the N-AQ schools ranked the other N-AQ teams. If there is no bias present, the means of the CDS of the two groups of coaches would be equal to each other. In order to control for the previous bias that we have demonstrated, we removed how an N-AQ coach votes on his own team and teams within his conference. For example, for Gary Patterson, the head coach of Texas Christian University (TCU), we analyzed his voting record for all the schools from C-USA, MAC, Sun-Belt and WAC, but did not include his voting record on schools from the MWC, the conference that Patterson’s TCU team played in during this time period, or his voting record for TCU. We performed this test on each of the six years, individually, as well as collectively. The results are presented below in Table 5.
In five of the six years, there was a statistically significant bias (p < .05). The largest amount of bias occurred in 2007, when AQ coaches ranked N-AQ teams an average of 1.92 spots below the positions assigned them by N-AQ coaches. The only year without bias was in 2009. An investigation of this year showed a couple of possible explanations. First, in two of the three previous years, N-AQ teams had significant BCS bowl wins. For example, after the 2006 regular season, Boise State defeated, then #8, Oklahoma in the Fiesta Bowl, and after the 2008 regular season, Utah beat, then #4, Alabama in the Sugar Bowl. Second, during the first few weeks of the 2009 season, when teams generally play out-of-conference games, several teams from N-AQ conferences had wins over good teams from AQ conferences: TCU beat a Clemson team that would win nine games and go on to win their division in the ACC, Boise State beat the #16 ranked Oregon Ducks, a team that won the Pac 10 and went on to play in the Rose Bowl, and BYU beat then #3 ranked Oklahoma. These high profile wins may have played a significant role in reducing the bias against N-AQ teams.
When the six-year period is looked at collectively, the AQ coaches ranked the N-AQ teams 0.80 places lower than the N-AQ coaches ranked those same teams (p < .001). While this result might, at first, seem like a small margin, recall that as Table 1 shows, in an average year, there are 10.5 more AQ coaches than N-AQ coaches voting–and the resulting bias can thus have a significant effect on the overall point totals and rankings.
### Discussion
This study demonstrated that coaches who are selected to vote in the *USA Today Coaches’ Poll* are subject to at least three different kinds of bias. First, coaches are biased toward their own teams. On average, coaches rank their own school 2.32 positions higher than do their peers. Indeed, the effect is so prevalent that 92.1% (or 82 out of 89) of coaches whose school finished in the top 25 ranked their own school higher than the average of the other coaches. In two years, 2007 and 2010, every single coach ranked their team higher than its final position. Twenty-eight of the 89 coaches (31.5%) ranked their school at least three positions higher than the average of their peers, and 11 of 89 (12.4%) voted their team at least five positions higher. One coach even voted his team 9.71 positions higher than the average of the other coaches’ rankings. In contrast, the maximum amount a coach ranked his team lower than did his peers was 1.18 positions. This bias seems to be a natural phenomenon. Social psychologists have extensively studied the concept of illusory superiority (11, 12), which describes how an individual views him or herself as above average, in comparison to their peers.
The second form of coaches’ bias found was bias toward their own conference. Over the six-year period from 2005 to 2010, coaches voted their conference members 1.19 positions higher than their average ranking. Representative examples of this type of bias are worth discussing. For example, in 2009, Mississippi received 87.5% of their total points from SEC conference coaches, who made up less than 12% of the voters. Similarly, in 2008, the Iowa Hawkeyes received 62% of their votes from Big Ten coaches, who made up less than 10% of the voting population. Further evidence of this effect is apparent when you compare two teams that finished very close in the rankings. For example, in 2009, Oregon and Ohio State were #7 and #8, respectively, in the poll, and they were separated by only 19 points. All five PAC 10 coaches voted Oregon ahead of Ohio State, while four of the five Big 10 coaches voted Ohio State ahead of Oregon (Interestingly, Jim Tressel, the coach of the Buckeyes was the only Big 10 coach to put Oregon ahead of Ohio State). A similar phenomenon happened in 2010 when Oklahoma and Arkansas were tied for the #8 ranking. Six of the seven Big 12 coaches voted Oklahoma higher, while five of the six SEC coaches voted Arkansas higher.
When comparing the bias across conferences, the WAC was found to be the most biased voting their own teams on average 2.97 places higher. Perhaps, this is due to the WAC coaches trying to overcompensate for the perceived bias that other voting coaches have against this N-AQ conference. In 2007, Hawaii went undefeated, yet finished #10 in the overall rankings behind seven teams from AQ conferences that had two losses, in large part due to the voting of AQ coaches. A similar result occurred in 2006 when Boise State went undefeated and finished #9 in the rankings behind three AQ teams with two losses.
The third form of bias discovered in this research was that of coaches toward N-AQ conference teams. Looking more closely at the numbers shows that while this bias does exist, it seems to be diminishing over time. The effect was at its highest in 2007 when AQ coaches ranked N-AQ teams 1.92 positions lower than did the N-AQ coaches. As previously mentioned, there was no significant difference in the CDS with regard to N-AQ teams in 2009, and this might be due to some significant wins N-AQ schools have had over AQ schools in recent years. Moreover, it will be interesting to see if the N-AQ bias is further reduced in the 2011 season after TCU’s defeat of Big 10 Champion Wisconsin in the 2011 Rose Bowl, which led the Horned Frogs to a #2 ranking in the final standings–the highest for any N-AQ team during the period we surveyed. The Rose Bowl win brought the N-AQ teams to a very impressive five wins to two losses in their BCS Bowl appearances. Their 71.4% winning percentage is higher than that of any of the AQ conferences.
There are a number of limitations to this study. For one, the data was limited to the final regular season *USA Today Coaches’ Poll*, as that is the only data made public. If more data is provided in the coming years, future researchers will be able to investigate whether coaches’ bias varies throughout the season. Greater availability of data will also allow researchers to use more sophisticated time series data analysis techniques, such as logistic regression. Secondly, the sample sizes for some of our subgroups was rather small. For example, because only one MAC team made the top 25 rankings, only five MAC coaches’ votes were used to assess the MAC’s own-conference bias. As more data is collected over time, and possibly more MAC teams make the top 25 poll, future researchers can replicate this study on a larger sample.
There are many fruitful areas remaining for future researchers to continue exploring bias in the Coaches’ Poll. Researchers can analyze where bias is the strongest – are coaches most biased when ranking teams in the top third of the standings, the middle third, or the bottom third, etc.? Previous research has shown that TV exposure impacts how media members vote (4); future researchers can determine whether it has any effect on the way coaches vote. The 2011 and 2012 seasons will see a shift in conference membership. Future researchers can attempt to discover what effect this has on bias. One particular study could examine Utah and TCU–two N-AQ teams who are moving to AQ conferences, the PAC 10 and the Big East, respectively. Will AQ coaches now see these teams as AQ teams or will they continue to see them, and thus penalize them, as being N-AQ teams? Finally, a last ripe area for exploration involves gathering the coaches’ opinions on the subject. Do coaches think that they themselves are biased? Do they think their colleagues are biased? And if coaches do think other coaches are biased, do they try to compensate for it?
### Conclusion
One thing is certain. The current BCS system has flaws, which leads to frequent fan and media criticism. While every system, including a playoff, has advantages and disadvantages, the BCS should continually evaluate itself in an effort to make improvements. If it does not, the scrutiny will only increase over time. For example, Wetzel, Peter and Passan’s 2010 book, Death to the BCS, has garnered much attention in the media. The authors refer to the BCS as an “ocean of corruption: sophisticated scams, mind-numbing waste, and naked political deals” (19). In fact, after reading this book, Dallas Mavericks’ Owner, Mark Cuban, formed his own company in late 2010, in an effort to create a play-off system that would challenge the BCS in the future (13).
In our opinion, BCS officials should consider making several changes. For one, they should use an email-based ballot to make it easier for coaches to vote, instead of the antiquated phone-in ballot system currently used. Moreover, they should not require all ballots to be turned in so soon after the weekend games. Coaches simply do not have enough time to thoroughly analyze all of the teams within 24 hours of finishing their games. The BCS could consider moving the voting deadline to later in the week. Secondly, coaches should not be allowed to vote for their own team–if this rule were implemented own-school bias would be eliminated. These last two recommendations are not new; both were made by Gallup when they were hired by the AFCA to examine the Coaches’ Poll in 2009 (20). While the AFCA decided not to implement them, we feel that given the dollars involved in the BCS rankings, these would be easy improvements to the system. Lastly, why not let every FBS coach vote? Normally, a sample or sub-set of the population is used due to the expense of a census. But, in this case, the population is not very large, with only 120 FBS coaches, nor is the process very complex or time-consuming. By allowing all coaches to vote, it may help reduce the amount of own-school benefit that about half of the teams are currently receiving. Moreover, as most conferences are roughly the same size, this measure would also help reduce the disparity in the number of voters from each conference, thus minimizing the effect of own-conference bias.
Overall, our research has highlighted some important issues with the Coaches’ Poll. Bias in voting has occurred in the political arena in many different forms (16) and researchers have discovered that the amount of information voters possess can impact voting preferences (17). Perhaps the AFCA could do the same with voting coaches using our research results. If the coaches were to see how much bias occurs and the different forms of bias that are present in the voting, they may be encouraged to vote more objectively.
Under the current system, we found three different forms of bias present in the *USA Today Coaches’ Poll*: bias toward own-team, bias toward own-conference and bias toward teams in N-AQ conferences. These are significant findings as the Coaches’ Poll is an important part of the BCS standings that accounts for one-third of the BCS formula; a formula that, in turn, can mean the difference between a team going to a bowl with a payout of $17 million versus a fraction of that amount.
### Application In Sport
This research has several applications for those in sport. For one, BCS and college football administrators now have a better understanding of the biases that coaches employ (intentionally or unintentionally) when voting. Hopefully, some changes, as suggested in our Conclusion section, can be made to improve the process. In addition, sport management researchers and students can continue to analyze the numbers in the future to investigate other forms and levels of bias, now that this study has provided a framework, namely the CDS, as a basis for voting comparisons.
### References
1. Anonymous (2009, November 6). AFCA to continue release of final regular season Coaches’ Poll ballots. Retrieved from <http://www.afca.com/ViewArticle.dbml?DB_OEM_ID=9300&ATCLID=204828450>
2. Associated Press. (2009, May 6). Coaches mull changes in football poll. Retrieved from <http://sports.espn.go.com/ncf/news/story?id=4147492>
3. BCSfootball.org. (n.d.). Retrieved from <http://www.bcsfootball.org>
4. Campbell, N. D., Rogers, T. M., & Finney, R. Z. (2007). Evidence of television exposure effects in AP top 25 college football rankings. Journal of Sports Economics, 8 (4), 425-434.
5. Coleman, B. J., Gallo, A., Mason, P. M., & Steagall, J. W. (2010). Voter bias in the associated press college football poll. Journal of Sports Economics, 11 (4), 397-417.
6. CollegeFootballPoll.com. Retrieved from <http://www.collegefootballpoll.com/2010_archive_bowls.html>
7. Collins, M. (2009, May 31). College football coaches choose darkness: Coaches Poll changes in 2010. Bleacher Report. Retrieved from <http://bleacherreport.com/articles/189684-college-football-coaches-choose-darkness-coaches-poll-changes-in-2010>
8. Dochterman, S. (2010, January 8). Orange Bowl visit, No. 7 final ranking worth millions in bonuses, raises to football program. The Gazette. Retrieved from <http://thegazette.com/2010/01/08/orange-bowl-win-worth-millions-in-bonuses-raises-to-football-program/>
9. Dodd, D. (2009, July 24). Spurrier’s fraudulent SEC vote makes fraud of coaches poll, too. CBS Sports. Retrieved from <http://www.cbssports.com/collegefootball/story/11982516>
10. Harris Interactive. (n.d.). Retrieved from <http://www.harrisinteractive.com>: <http://www.harrisinteractive.com/vault/HI-BCS-HICFP-FAQs-2010-10-15.pdf>
11. Hoorens, V. (1995). Self-favoring biases, self-presentation, and the self-other asymmetry in social comparison. Journal of Personality, 63 (4), 793-817.
12. Hornsey, M. J. (2003). Linking superiority bias in the interpersonal and intergroup domains. The Journal of Social Psychology, 143 (4), 479-491.
13. MacMahon, T. (2010, December 16). Mark Cuban exploring BCS alternative. ESPN Dallas. Retrieved from <http://sports.espn.go.com/dallas/nba/news/story?id=5924399>
14. Mandel, S. (2005, December 7). The real BCS controversy. Want evidence of bias? Just look at coaches’ votes. Sports Illustrated CNN. Retrieved from <http://sportsillustrated.cnn.com/2005/writers/stewart_mandel/12/07/mailbag/index.html>
15. Paul, R. J., Weinbach, A. P., & Coate, P. (2007). Expectations and voting in the NCAA football polls: The wisdom of point spread markets. Journal of Sports Economics, 8 (4), 412-424.
16. Sigelman, C. K., Sigelman, L., Thomas, D. B., & Ribich, F. D. (1986). Gender, physical attractiveness, and electability: An experimental investigation of voter biases. Journal of Applied Social Psychology, 16 (3), 229-248.
17. Taylor, C. R., & Yildirim, H. (2009, June). Public information and electoral bias. Retrieved from <http://econ.duke.edu/~yildirh/elections.pdf>
18. *USA Today*. (2011, January 11). Top 25 Coaches’ Poll. Retrieved from <http://www.usatoday.com/sports/college/football/usatpoll.htm>
19. Wetzel, D., Peter, J. & Passan, J. (2010). Death to the BCS: The Definitive Case Against the Bowl Championship Series. Retrieved from <http://www.deathtothebcs.com>.
20. Whiteside, K. (2009, May 28). Football coaches to keep poll ballots secret starting in 2010. *USA Today*. Retrieved from: <http://www.usatoday.com/sports/college/football/2009-05-27-coaches-poll-votes_N.htm?loc=interstitialskip>
21. Witte, M. D., & Mirabile, M. P. (2010). Not so fast, my friend: Biases in college football polls. Journal of Sports Economics, 11 (4), 443-455.
### Figures
#### Figure 1
A Model of Coaches’ Bias in Voting
### Tables
#### Table 1
Voter Composition by Conference
#### Table 2
T-Test Results of Own-Team Bias
| Year | Mean Difference | Std. Error | df | t-stat | Significance |
|---|---|---|---|---|---|
| 2005 | 1.69 | .40 | 15 | 4.23 | .001 |
| 2006 | 2.51 | .47 | 18 | 5.29 | .000 |
| 2007 | 3.12 | .57 | 13 | 5.49 | .000 |
| 2008 | 1.61 | .47 | 12 | 3.43 | .005 |
| 2009 | 2.63 | .83 | 11 | 3.19 | .009 |
| 2010 | 2.38 | .52 | 16 | 4.59 | .000 |
| All Years | 2.32 | .22 | 90 | 10.57 | .000 |
#### Table 3
T-Test Results of Own-Conference Bias
| Year | Mean Difference | Std. Error | df | t-stat | Significance |
|---|---|---|---|---|---|
| 2005 | 1.03 | .18 | 129 | 5.75 | .000 |
| 2006 | 0.93 | .17 | 121 | 5.59 | .000 |
| 2007 | 1.95 | .21 | 130 | 9.43 | .000 |
| 2008 | 1.09 | .19 | 125 | 5.69 | .000 |
| 2009 | 1.09 | .21 | 120 | 5.10 | .000 |
| 2010 | 0.98 | .16 | 116 | 6.25 | .000 |
| All Years | 1.19 | .08 | 746 | 15.32 | .000 |
#### Table 4
Descriptive Statistics for Own-Conference Bias
| Conference | N | Mean Difference | Std. Error |
|---|---|---|---|
| ACC | 111 | 1.02 | .17 |
| Big 10 | 119 | 1.20 | .17 |
| Big 12 | 128 | 0.68 | .17 |
| Big East | 52 | 1.61 | .26 |
| C-USA | 10 | 0.49 | .45 |
| MAC | 5 | -1.19 | .93 |
| MWC | 42 | 1.48 | .30 |
| Pac 10 | 79 | 1.29 | .29 |
| SEC | 175 | 1.25 | .17 |
| WAC | 26 | 2.97 | .48 |
#### Table 5
T-Test Results of AQ vs. N-AQ Bias
| Year | AQ Mean | N-AQ Mean | difference | t-stat | significance |
|---|---|---|---|---|---|
| 2005 | -0.41 | 0.45 | -0.85 | -1.95 | .056 |
| 2006 | -0.57 | 0.54 | -1.11 | -3.74 | .000 |
| 2007 | -1.05 | 0.87 | -1.92 | -3.88 | .000 |
| 2008 | -0.29 | 0.39 | -0.68 | -2.48 | .014 |
| 2009 | -0.34 | 0.01 | -0.35 | -1.36 | .176 |
| 2010 | -0.35 | 0.20 | -0.55 | -2.75 | .006 |
| All Years | -0.46 | 0.34 | -0.80 | -6.38 | .000 |
### Appendices
#### Appendix A
Coach Composition of Coaches’ Poll
#### Appendix B
Team Rankings in the Coaches’ Polls Analyzed
### Authors
#### Michael Stodnick, Ph.D.
Assistant Professor, College of Business
University of Dallas
#### Scott Wysong, Ph.D.
Associate Professor, College of Business
University of Dallas
### Corresponding Author
Scott Wysong, Ph.D.
Associate Professor, College of Business
University of Dallas
1845 E. Northgate Dr.
Irving, TX 75062
<swysong@gsm.udallas.edu>
972-721-5007
Exploring the Physical Health Behavior Differences between High and Low Identified Sports Fans
### Abstract
The purpose of this study was to investigate physical health-compromising behavior differences of sports fans (highly identified) to those of non-sports fans (less identified). The justification for the study was that if highly identified sports fans were found to engage in elevated health risk behaviors, targeting this group would represent a significant opportunity for health policy makers to achieve a significant impact on the health and wellness of a large segment of Americans while at the same time reducing the costs associated with obesity and unhealthy living practices. Electronic surveys were forwarded to individuals at an American University located in the mid-south region of the country. A sample of 515 participants responded. Highly identified sports fans had significantly higher health risk behaviors than non-sports fans on a range of health behavior measures, including: higher fat consumption, more fast food consumption, less vegetable consumption, greater consumption of refined as opposed to whole grains, and an increased amount of alcohol consumed on days they chose to drink. Additionally, using height and weight data to calculate Body Mass Index (BMI), highly identified sports fans were found to have a higher BMI. Recommendations for future research and applications of the findings to sport are presented.
**Key words:** health, behavior, identification, sport, marketing
### Introduction
According to the Centers for Disease Control and Prevention (9), chronic diseases such as heart disease, cancer, and diabetes are the leading causes of death and disability in the United States accounting for seven out of ten U.S. deaths annually. Approximately 133 million Americans have at least one chronic disease and this has increased dramatically over the last three decades with young Americans’ diagnosis of chronic diseases quadrupling over the past four decades (9). Also, worldwide, chronic diseases are reaching epidemic proportions, affecting individuals of all ages and nationalities with some 388 million people expected to die from one or more chronic diseases in the next ten years (10). Not only are chronic diseases devastating based on mortality rates but also bring with them high levels of morbidity which limit daily living and reduce quality of life.
Another consequence of an increase in the prevalence of chronic disease is the economic toll exerted on the economy of the United States. As a nation, the United States spends two trillion dollars per year on health care and it has been projected that more than 200 million Americans alive today will develop a chronic illness which in turn will equate to a cost of $1 in every $4 spent in the US going toward health care (1). This trend is also occurring internationally. Within the next ten years China, India, and the United Kingdom are projecting losses in national income of US$828 billion due to reduced economic productivity associated with chronic disease (10).
Risk factors associated with the development of chronic diseases such as high blood pressure, high blood cholesterol, smoking, being over weight or obese (BMI greater than 25.0), inactivity, and poor diet provides a depressing snap shot of the future development of chronic disease. Risk factor data elucidates the future chronic disease burden and provides information necessary for the development of preventive interventions (33). Lifestyle, behavioral risk factors, and social and environmental conditions have now become the key determinants of the public’s health (31). Controlling disease risk factors must be addressed as a major component in the fight against chronic disease development.
One of the primary ways health prevention workers seek to control disease risk factors and alter personal behaviors is by educating the public through social marketing initiatives. Andreasen (2) defined social marketing as “the application of commercial marketing technologies to the analysis, planning, execution and evaluation of programs designed to influence the voluntary behaviour of target audiences in order to improve their personal welfare and that of society” (p. 7). Distinguished by its emphasis on non tangible products such as ideas, attitudes, and lifestyle changes, social marketing has been described as a process serving to “increase the acceptability and ideas or practices in a target group, solve problems, introduce and disseminate ideas and issues, and as a strategy for translating scientific knowledge into effective education programs” (19, p. 2).
A key component of the social marketing process is market segmentation. The emphasis placed on market segmentation, or knowing one’s audience brings precision to audience analysis, allowing health prevention efforts to collect vital information for the formulation of better targeted and more effective messages leading to more appropriate message design, more effective message delivery, and better reception by the public (22). Reaching large, targeted segments of the U.S. population with appropriate marketing of risk reduction education and interventions can begin reducing the disastrous course of chronic disease development.
One particularly large, readily identifiable, and commercially lucrative segment of the U.S. population is the sports fan. Distinguished from the casual sports observer, a sports fan is defined as someone who is “interested in and follow(s) a sport, team, and/or athlete” (47, p. 2). Sports fans have long been the target of Corporate America, as marketers have understood the positive ‘return on investment’ (ROI) benefits associated with marketing their goods and services to sports fans through sponsorships and traditional advertising. Corporate executives choose to link their messages to the objects of sports fans’ attention to gain message credibility and increase message receptivity, as sporting events are well accepted and have a strong fan following. This study was designed to compare the health risk behaviors of sports fans and non-sports fans on the premise that those who have a heightened interest in following sports may be a perfect segment to which health prevention education efforts could be directed.
***Sports consumption and sports fandom in America.*** Sports for entertainment purposes have become an increasingly prominent leisure activity as well as an important part of the American economy in contemporary society. The sports business is one of the largest and fastest growing industries in the United States. A recent research report (26) estimated the size of the entire U.S. sports industry to be $414 billion as of 2010. The same publication reported that in 2010 annual company spending for sports advertising has reached $27.3 billion. The pervasiveness of sports fandom in contemporary society is even further highlighted by the continued increase in attendance figures, the amount and extensiveness of sports coverage through various forms of media such as radio, television, and print publications, as well the emergence of and use of new media technologies such as the internet and social networking (47).
Recognizing that both sports fans and non-sports fans are likely to consume, in some form or another, sports entertainment products, the study of the former as a unique market segment requires a distinction be made between sports fans and casual observers. An increasingly common psychological construct used to measure the degree to which one is a sports fan is team identification. Team identification refers to the extent to which a person feels psychologically connected to a team (47) and as the personal commitment and emotional involvement customers have with a sport organization (34). Concerning issues related to self-esteem and the self-concept, contemporary thinking on identification is rooted in the literature on social identity theory (35-37). Tajfel (1981) defined social identity as “the aspect of individuals’ self-concept which derives from their knowledge of their membership in a social group (or groups) together with the value and emotional significance attached to that membership” (35, p. 251).
Team identification is a useful construct for distinguishing between sports fans and non-sports fans because the degree to which one is attached or identified to a particular team reflects the extent to which the organization is linked to the self given its essentiality in facilitating utilitarian, experiential, or symbolic needs (26). For the highly identified individual, the role of team follower is a central component of their identity. These individuals readily present themselves as a fan of their team to others, view association with their team as a reflection and extension of themselves, and see the team’s successes and failures as their own (47). In contrast, for the casual observer, or lower identified person, the role of team follower is a peripheral component to self-concept. As a result, researchers examining the phenomena of sports fandom have reported that sports fans are more likely to spend a great deal more of time, energy, and resources following their teams than non-sports fans (11,44) and are more loyal to teams during periods of poor performance (24,41).
Investigating the link between sports fandom and health, researchers have consistently found team identification to have a positive relationship to measures of psychological health such as social self-esteem and social well-being, vigor, extroversion, and frequency of positive emotions, as well a negative relationship to loneliness and alienation (4,43,45,48). It was reasoned that identification with a sports team may perform an important psychological role for individuals in contemporary society (4). A strong identification with a specific sports team has been thought to provide a buffer from feelings of depression and alienation and fosters feelings of belongingness and self-worth as traditional social and community ties have declined in the wake of the erosion of the nuclear family and neighborhoods, faith in political institutions and religion, and increased geographic mobility and industrialization (4,12,20).
Despite the increased attention being given by scholars to the study of the psychological outcomes associated with sports fandom, there have only been a scant number of studies focused on the physical health of those who follow sports (3,8,18) and these studies have primarily focused on acute incidence of negative health events associated with watching a sports event. For example, Barone-Adesi, Vizzini, Merletti, and Richiardi (3) examined hospital admissions for acute myocardial infarction (AMI) among the Italian population during three international football competitions: the World Cup 2002, the European Championship 2004, and the World Cup 2006. They did not find an increase in the rates of admission for AMI on the days of football matches involving Italy in either the single competitions or the three competitions combined, and thus concluded the cardiovascular effects of watching football matches were small. Conversely, Carroll, Ebrahim, Tilling, Macleod, and Smith (8) examined hospital admissions for a range of diagnoses on days surrounding England’s 1998 World Cup football matches. The results indicated the risk of admission for AMI increased by 25% on the day of a home team loss in a big game and on the two following days. Kloner, McDonald, Leeka, and Poole (18) investigated changes in death rates when a local football team participated in and won the Super Bowl and when a local team participated in and lost the Super Bowl. Two events were examined, namely: 1) the January 20, 1980 game between the Los Angeles Rams and Pittsburgh Steelers (which Los Angeles lost); and 2) the January 22, 1984 game between the Los Angeles Raiders and Washington Redskins (which Los Angeles won). The researchers concluded the emotional stress of loss and/or the intensity of a game played by a sports team in a highly publicized rivalry such as the Super Bowl could trigger total and cardiovascular deaths.
These studies suggest that individuals who care about the outcome of a sporting event are more likely to experience negative acute health consequences as a result of the stress associated with the experience of watching their team. However, what is lacking in the literature is a discourse on the health related lifestyle behaviors of sports fans that may ultimately lead to the acute incidents described above.
The purpose of the present study was to investigate physical health-compromising behavior differences of sports fans (highly identified) to those of non-sports fans (less identified). If highly identified sports fans are found to engage in elevated health risk behaviors, targeting this group may represent a significant opportunity for health policy makers to achieve a significant impact on the health and wellness of a large segment of Americans while at the same time reducing the costs associated with obesity and unhealthy living practices.
### Methods
#### Participants
Using an electronic survey distribution software platform, electronic surveys were forwarded to community members at a University located in the U.S. mid-south who were in possession of a valid email account. Participants accessed the survey by clicking on a link contained in the body of an introductory email message. Email reminders were sent at two and four week intervals following the initial invitation. A sample of 515 students took part in the investigation.
#### Procedures
Upon clicking on the hyperlink contained in the body of the email communication participants were taken to the survey homepage where further instructions were provided and consent was sought. Continuation to the first section of the survey questionnaire was taken as consent to participate. In total, the survey comprised of four sections. The first section contained one question asking participants to identify their absolute favorite sports team. The purpose of this question was to have participants self-report a subject as a frame of reference to use when answering the questions contained in section two of the survey.
The second section comprised the team cognitive-affective identification subscale from the Team Identification Scale (TIS) developed and tested by Dimmock, Grove, and Eklund (11). Cognitive-affective identification was operationalized as one’s knowledge of membership to a group and the emotional significance of membership to that group. The scale contained 8 Likert-scale items with response options ranging from 1 (strongly disagree) to 6 (strongly agree). Thus, higher numbers represent greater levels of identification. A sample item from cognitive-affective identification scale read, “When I talk about my favorite team, I say ‘we’ rather than ‘they’”. Acceptable test-retest reliability coefficients for the cognitive-affective subscale (r = .72, p = .01) were reported (11). As noted above, subjects targeted the team they personally identified in section one when completing the team identification scale. The eight items comprising the cognitive-affective team identification scale were summed and then averaged to form a single index of identification (Cronbach’s alpha = .91). A median split was performed on the participants’ scale scores to establish two groups: participants with a low level of identification with the team (n = 255, scale range = 1 to 3.49) and participants with a high level of team identification (n = 260, scale range = 3.5 to 6).
The third section of the survey contained eleven questions assessing participants’ self-reported health risk behaviors, including: two questions related to physical activity, six questions related to eating practices, and one question each for alcohol use, tobacco use, and Sexually Transmitted Diseases (STD) and Acquired Immunodeficiency Syndrome (AIDS) risk. The questions in this section were adopted from the Comprehensive Assessment Plus Personal Wellness Profile developed by Wellsource, Inc. The Personal Wellness Profile has been found to be a reliable and valid questionnaire to assess an individual’s level of wellness in clinical and non-clinical setting (7).
The fourth and final section of the survey assessed the participants’ demographic information including position at the university (i.e., student, staff, faculty, or administrator) age, sex, as well as two physical descriptive characteristics, namely: height and weight. Height and weight data enabled the researchers to calculate each participant’s BMI. BMI, which is a ratio of weight in proportion to height, was calculated from self-reported weight and height data using the imperial BMI formula (weight in pounds multiplied by 703 over height in inches squared). BMI was defined using the following standardized categories: underweight (BMI = < 18.5); normal weight (BMI = 18.5 – 24.9); overweight (BMI = 25 – 29.9); obese (BMI of 30 or greater).
#### Data Analysis
The analysis involved testing for physical health risk behavior differences in the measure of identification for those participating in the research. PASW Statistics program version 18 was used to compute a series of several independent ANOVAs for this purpose.
### Results
#### Descriptives
Descriptive analysis (frequencies and percent) of variables under study is displayed in Table 1. The majority of respondents were female (64.3%) and married (39.8%). The majority of those responding were aged 18 to 24 (34.6%) followed by 30 to 39 (17.3%) and 25 to 29 (15.9%). In terms of ethnicity, 76.3% of the respondents were Caucasian and 13.8% were African American. Finally, student respondents represented the largest group in the sample (69.7%), followed by faculty (12.6%) and staff members (11.1%). Means and standard deviations for team identification as a function of variables under study are displayed in Table 2.
#### Group Differences
Analysis of variance (ANOVA) results for team identification (high identification and low identification) as a function of variables under study are reported in Table 3. The results of the one-way ANOVA revealed significant differences between the self-reported health behaviors of low identified individuals and high identified individuals for each of the following dimensions: breakfast frequency, F(1,513) = 5.35, p < .05; fat intake, F(1,513) = 4.13, p < .05; fast food consumption frequency, F(1,513) = 4.17, p < .05; vegetable consumption frequency, F(1,513) = 3.34, p < .10; breads and grains consumption, F(1,513) = 3.54, p < 1.0; and alcohol consumption, F(1,513) = 16.63, p < .05. Additionally, the ANOVA results revealed a significant difference in the BMI of low identified individuals and high identified individuals, F(1,513) = 5.36, p < .05. For each of the results reported above, analysis of the dimension means for each group indicated that high identified sports fans have poorer health related behaviors than low identified subjects. No significant group differences were found for the following self-reported health behaviors: aerobic exercise frequency, strength training exercise frequency, unhealthy snack consumption, smoking frequency, and risk factor for AIDS and STDs.
### Discussion
The objective of this study was to develop an understanding of the health related lifestyle behavior disparities among sports fans and non-sports fans using team identification as a proxy for sports fandom. The results indicated that sports fans have a significantly higher BMI than do non-sports fans and engage in riskier health related behaviors than do their non-sports fan counterparts on a range of measures. All of the measures on which the two groups differed related to diet and food consumption choices. Sports fans were found to eat breakfast less often than non-sports fans, consume foods higher in fat more often, consume fast food on a more regular basis, consume vegetables less often, consume refined grains as opposed to whole grains more often, and consume more alcoholic beverages on the days they chose to drink than do non-sports fans. The two groups did not significantly differ on the following measures: aerobic exercise frequency, strength training exercise frequency, unhealthy snack consumption, smoking frequency, and risk factor for STD and AIDS. It is important to note that no differences were found between the two groups on the measures of physical activity because both groups were equally inactive.
Given that this study represents, to our knowledge, the first attempt to scientifically investigate health related lifestyle behaviors of sports fans, there are not any direct explanations in the literature to explain why the observed differences exist. However, an examination of the literature related to the lifestyle of sports fans in general may provide some clues, or insight, into possible causes. The level of identification one has to an organization has been found to relate to the nature of a consumer’s interaction with the organization (40). Strongly identified sports fans often make heavy financial and/or time commitments toward following their favorite team and devote significant portions of their day to that pursuit (11,44). This time commitment includes time reading about one’s favorite team on the internet and in magazines, listening to the radio, watching the team play and also engaing in discussion about the team with others.
Additionally, there is evidence in the literature demonstrating an inverse relationship between mass media consumption (viewing hours), and intake of healthy food choices such as fruits and vegetables (6). It was suggested this relationship may be the result of the replacement of healthy foods by foods highly advertised on television (6). It is conceivable this rationale may apply for highly identified sports fans, who have been found to exhibit a bias towards the brands and products that sponsor their favortite teams and events than do lower identified indivuals (14,30).
### Conclusion
The findings from this study should be interpreted in light of several limitations that could be addressed in follow-up research on health risk behaviors of sports fans. Among them, due to the cross-sectional nature of this investigation, errors in recall by the study participants may be present. Additionally, as self-report behaviors were used to measure the variables under study, the reliability in the accuracy of participant responses may be questioned. The underreporting of energy, or food intake, using self-report instruments has been documented in the literature (17,21,23). Finally, the present findings might only be generalizable to a primarily student population in U.S. mid-south.
To address issues related to generalizability and to verify the results found here, future researchers may wish to replicate this study using a national sample of sports fans. Additionally, future research should examine the reasons why health behavior disparities exist between those persons who self-report having a higher level of identification to a sports team than those reporting a lower identification. Finally, to improve education efforts, future research may also be conducted for the purposes of gaining an understanding of sports fans attitudes about health related behaviors and health in general.
### Applications In Sport
The results of the current study suggest health educators and policy makers seeking to make a significant positive contribution to the fight against preventable chronic illnesses resulting from unhealthy lifestyles would do well to follow the lead of corporate America in targeting the large and identifiable segment of the population who are identified sports fans. Writing on the societal and environmental factors affecting food choice and physical activity, Booth, Mayer and Sallis (5) noted changes in these behaviors require intervention and commitment to action at multiple levels and that education based obesity-prevention strategies are most effective when there exists environmental modifications supported by partnerships with relevant sectors outside traditional health domains, including researchers, educators, government, and industry. Thus, educating sports fans about healthy living practices must involve collaboration with the objects of fans’ attention, namely the college athletic departments, leagues, teams, and athletes they follow. These sports organizations and entities already very well recognize the importance of community outreach as a part of their business models. Notwithstanding the desire to positively contribute to the betterment of the communities in which they are situated, sport organizations engage in socially responsible initiatives for strategic reasons as well. Organizations that do ‘good’ have been found to gain a competitive advantage in the marketplace and are more likely to succeed than those who do not (27,28). As a result, many sport organizations have implemented focused strategies towards achieving a competitive marketplace advantage by becoming ‘good’ corporate citizens. Thus, partnering in programs designed to educate their most devoted followers about strategies towards achieving a healthy lifestyle would serve the dual role of contributing to the overall success of the organization while at the same time positively impacting the health of those in the communities they serve.
### Tables
#### Table 1
Descriptive analysis (frequencies and percent) of classification variables.
| Variables | n | % | |
|---|---|---|---|
| Sex | Male | 178 | 35.7 |
| Female | 320 | 64.3 | |
| System Missing | 17 | 3.3 | |
| Total | 515 | 100.00 | |
| Age | 18-24 | 178 | 34.6 |
| 25-29 | 82 | 15.9 | |
| 30-39 | 89 | 17.3 | |
| 40-49 | 72 | 14.0 | |
| 50-59 | 48 | 9.3 | |
| 60+ | 27 | 5.2 | |
| System Missing | 19 | 3.7 | |
| Total | 515 | 100.00 | |
| Relationship Status | Single | 134 | 26.0 |
| In a Relationship | 127 | 24.7 | |
| Married | 205 | 39.8 | |
| Seperated | 3 | 0.6 | |
| Divorced | 28 | 5.4 | |
| Widowed | 6 | 1.2 | |
| System Missing | 12 | 2.3 | |
| Total | 515 | 100.00 | |
| Ethnicity | Black | 71 | 13.8 |
| White | 393 | 76.3 | |
| Hispanic | 13 | 2.5 | |
| Asian | 12 | 2.3 | |
| Native American | 4 | 0.8 | |
| Other | 7 | 1.4 | |
| Missing | 15 | 2.9 | |
| Total | 515 | 100.00 | |
| Institution Status | Student | 359 | 69.7 |
| Staff Member | 57 | 11.1 | |
| Faculty | 65 | 12.6 | |
| Administrator | 8 | 1.6 | |
| Dual Role | 16 | 3.1 | |
| System Missing | 10 | 1.9 | |
| Total | 515 | 100.00 | |
#### Table 2
Means and standard deviations for team identification as a function of variables under study.
| Dependant Variable | n | Mean | S.D. | Std. Error |
|---|---|---|---|---|
| Body Mass Index (BMI) | ||||
| Low identifiers | 255 | 25.09 | 5.52 | .796 |
| High identifiers | 260 | 2.81 | 2.37 | .147 |
| Aerobic Exercise | ||||
| Low identifiers | 255 | 2.94 | 2.44 | .153 |
| High identifiers | 260 | 2.81 | 2.37 | .147 |
| Strength Training | ||||
| Low identifiers | 255 | 2.70 | 1.61 | .101 |
| High identifiers | 260 | 2.56 | 1.61 | .100 |
| Eat Breakfast | ||||
| Low identifiers | 255 | 1.42 | 1.67 | .104 |
| High identifiers | 260 | 1.76 | 1.69 | .105 |
| Healthy Snack Consumption | ||||
| Low identifiers | 255 | 1.36 | 1.27 | .079 |
| High identifiers | 260 | 1.40 | 1.31 | .081 |
| Fat Intake | ||||
| Low identifiers | 255 | 1.80 | 1.90 | .119 |
| High identifiers | 260 | 2.15 | 2.05 | .127 |
| Fast Food Consumption | ||||
| Low identifiers | 255 | 1.53 | 1.30 | .081 |
| High identifiers | 260 | 1.77 | 1.32 | .082 |
| Vegetable Consumption | ||||
| Low identifiers | 255 | 2.53 | 1.64 | .103 |
| High identifiers | 260 | 2.80 | 1.81 | .112 |
| Refined Grains Consumption | ||||
| Low identifiers | 255 | 2.73 | 1.47 | .083 |
| High identifiers | 260 | 3.29 | 1.87 | .110 |
| Alchohol Consumption | ||||
| Low identifiers | 255 | 2.73 | 1.47 | .083 |
| High identifiers | 260 | 3.29 | 1.87 | .110 |
| Smoking Behavior | ||||
| Low identifiers | 255 | 1.16 | 1.92 | .120 |
| High identifiers | 260 | 1.29 | 2.12 | .131 |
| STD Risk Behavior | ||||
| Low identifiers | 255 | 0.91 | 1.38 | .086 |
| High identifiers | 260 | 1.06 | 1.44 | .089 |
#### Table 3
Analysis of Variance (ANOVA) Results
| Independent Variable
Team Identification |
|||
|---|---|---|---|
| Dependent Variables | df | F | p |
| Body Mass Index (BMI) | 1 | 5.36 | .021 |
| Aerobic Exercise Frequency | 1 | .352 | .553 |
| Strength Training Exercise Frequency | 1 | .928 | .336 |
| Eat Breakfast Frequency | 1 | 5.35 | .021 |
| Unhealthy Snack Consumption | 1 | .143 | .705 |
| Fat Intake (High vs. Low) | 1 | 4.13 | .043 |
| Fast Food Consumption Frequency | 1 | 4.17 | .042 |
| Vegetable Consumption Frequency | 1 | 3.34 | .068 |
| Breads and Grains Consumption (Refined vs. Whole) | 1 | 3.54 | .061 |
| Alchohol Consumption | 1 | 16.63 | .000 |
| Smoking Frequency | 1 | .545 | .461 |
| Risk Factor for AIDS and STDs | 1 | 1.571 | .211 |
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### Corresponding Author
Daniel R. Sweeney, PhD.
Department of Health Sciences
University of Arkansas at Little Rock
2801 S University Ave
Little Rock, AR 72204
<drsweeney@ualr.edu>
501-683-7575
Daniel Sweeney is an assistant professor of sport management and Donna Quimby an associate professor of exercise science and chair of the department.
Motivation and Goal Orientations of Master Games Participants in Hong Kong
### Abstract
The purpose of this study was to investigate the participation motives and goal orientations of participants in the Hong Kong Master Games. The participants were 108 men and 52 women (N=160). The age range of participants was 35 to 77 years old (M= 46.2, SD = 9.2). They were divided into three age groups (30-39 years old, n=32; 40-49 years old, n=96; above 50 years old, n=32). The _Participation Motivation Inventory_ (Gill, Gross & Huddleston, 1983), and the _Task and Ego Orientations Questionnaire_ (Duda & Whitehead, 1998) were utilized. The top five participation motives were fun, affiliation/friendship, fitness, skill development and achievement/status. The participation motives and the goal orientations for men and women were similar. The oldest adults had significantly higher scores on the eight participation motives: fun, skill development, fitness, team atmosphere, achievement/ status, affiliation /friendship, energy release and miscellaneous than the younger and middle age adults. The mean score on task orientation was higher than the ego orientation for all participants. The empirical results of this Hong Kong study support earlier studies (Vogel, Brechat, Leprete, Kaltenbach, Berthal & Lonsdorfer, 2009) that strongly encourage physical activity leaders to design sport and physical activity programs for adults in order to enhance their physical, social, psychological and mental well being.
**Key Words:** motivation, physical activity, task and ego orientation, master games
### Introduction
Historically, sociologically, politically, culturally and now medically, sport and physical activity has a long history of contributing to the overall evolution and positive growth of the human species (Bloom, Grant, & Watt, 2005). More recently a very strong body of evidence has been developed to support the theory that regular physical activity contributes to the overall health of the human species throughout the lifecycle- from childhood to old age, supporting the old adage that it is “never too early nor too late” to participate in sport and physical activity (Shepherd, 1995, Levy, 1998; Galloway & Jokl, 2000; Colcombe & Krame, 2003, U.S. Department of Health and Human Services, 2008). Furthermore, physical inactivity has serious health, economic and political implications in a world where health is at the core of a vibrant and prosperous society (Commonwealth of Australia, 2000; Conference Board of Canada, 2005). As the population of older adults in developed nations is increasing, “aging well” and successful active aging programs have become a critical area of scientific study related to geriatric health care (Graves, 2002) .
In Hong Kong, the proportion of population aged 35 to 64 and above 65 has increased from 28.3% and 3.2% in 1961 to 46.6 % and 12.8% in 2009 respectively (Hong Kong Census and Statistics Department, 2010). Sport and physical activities play such an important role in keeping the ever aging population healthy, governments at all levels, pay more attention and efforts to promote the concept on “Sport for all” to the general public (Canadian Fitness and Lifestyle Research Institute, 2005; Cheung, 2009).
For the past twelve years, the Leisure and Cultural Services Department has been organizing the Master Games to promote a physically active lifestyle for Hong Kong citizens. The emphasis of these games has been on participation and enjoyment rather than winning prizes (Leisure and Cultural Services Department, 2004). However, it also happened that there was very limited research conducted on investigating the motivations underlying participation among individuals aged 35 and above taking part in the Master games. However, if we hope to understand why Hong Kong people participate in sport and physical activity while other become couch potatoes and strain the health care system, motivational research of this kind is badly needed. Therefore, this study was designed to investigate these critical motivational determinants behind the participants in the Master Games in Hong Kong.
#### Motivations in Sports
Motivation comes from the Latin word “movere” which means “to move” and it is the energy or intensity underlying behavior (Carron, 1980). Motivation refers to those personality factors, social variables, or cognitions that come into play, enter into competition to attain some standard of excellence.
Gill, Gross and Huddleston (1983) had identified the motivations into eight main factors, which were achievement/ status, team atmosphere, fitness, energy release, skill development, affiliation/ friendship, fun and miscellaneous (e.g. like to use the equipment). Researchers stated that enjoyment, interest and competence motives were the internal factors which played an important role in motivating individuals to participate in sports (Scanlan, Stein & Ravizza, 1989a, 1989b; and Frederick & Ryan, 1993). Scanlan, Stein and Ravizza, (1989b) found that social and life opportunities (affiliation/friendship motive) and social recognition (factor of achievement/ status) were the other important factors to motivate people to take part in physical activity.
#### Goal Orientations in Sports
The Achievement goal theory was originally developed to explain educational achievement. This theory was widely applied in the context of sport and exercise researches (Lavallee, Kremer, Moram, & Williams, 2004). The two main achievement goals (task goal orientation and ego goal orientation) are the factors which determine a person’s motivation (Weinberg & Gould, 2003). Nicholls (1989) believed that goal orientations reflected an individual’s view of the world and were conceptually related to beliefs held on the cause of success. In addition, Ferrer and Weiss (2000) also stated that the strongest predictors of intrinsic motivation, effort and persistence were task goal orientation, perceived competence, and learning climate.
Individual with task-oriented goals focuses on self-referenced perceptions of personal competence and personal development, emphasis on mastery of skills, working hard, developing lifetime skills and improving from one point of time to the next. On the other hand, an ego-oriented goal individual focuses on surpassing or exceeding the performance of others and preferably with low effort (Duda & Nicholls, 1992).
There is a positive relationship between task-oriented and intrinsic motivation. Because the sport experience is an end in itself, by its defining features, the task-oriented individual focuses on the process rather than the competition outcomes when participating in sport. While the ego-involved goal perspective is more likely to decrease intrinsic motivation as the individual’s perceived ability and self-confidence are tied to how he/she compares with others (Duda, Chi, Newton, Walling & Catley, 1995, Cox 2007). For instance, task-oriented individuals who are assumed to experience intrinsic motivations and would like to choose a challenging task, show off their effort and have a strong work ethics as their motives, are more likely to focus on the skill development, fitness and team membership. The ego-oriented participants are assumed to show minimal effort, have low perceived competence, and more likely to protect self-worth with motives focusing on competition and recognition/ status (White & Duda, 1994; Robert & Treasure, 1995; and Roberts Treasure & Balague, 1998).
#### Gender Differences in Sports Participation
In motivation research, men valued self-competitive, reward, and skill improvement as their participation motives in physical activities. Whereas women valued self-expression, stress reduction, weight loss and relaxation, especially in weight control and appearance motives (Mathes, McGiven & Schneider, 1992). Furthermore, Frederick and Ryan (1993) reported that the main distinction of gender differences in sport participation was that men rated health and fitness, competition and challenge as the top participation motives; while women rated tension release, body-related and social factors as their top participation motives.
In goal achievement, most of the previous researches revealed that women had significantly higher scores in task orientation than men, and men had significantly higher score in ego orientation (Duda, 1989; Newton & Duda, 1993; Walling & Duda, 1995).
#### Age Differences and Sports Participation
Individuals have different reasons for participating in sports and physical activities. Rudman (1989) had investigated members enrolled in fitness program of a private sport club and reported that the younger participants (aged under 34 years) took part in sports because of the psychological benefits such as dealing with stress related to work and enhancing their physical attractiveness. For the participants of middle age (35-49 years), their participation motives were more the philosophical with ideological reasons such as family obligations and enjoyment/ fun. For the oldest participants (above 50 years), their participation motives were psychological and social reasons such as feeling younger and social networking with family members and friends.
Moreover, Kleiber and Kelly (1980) identified that both the younger adults with ages approximately 20 to mid-30s and the older adults who were above 60 years old chose the social goals as a reason for participating in sports, while the middle-age adults (35-50 years old) identified their participation goals as seeking close personal relationship. In addition, Brodkin andWeiss (1990) found that all the younger adults (23-39 years), the middle-aged adults (40-59 years) and the older adults (above 60 years) rated skill improvement, fun and being active as their main participation motives while engaging in swimming competition. They also found that being with friends was the most important motive for both the middle-age and the older adults.
Goal orientations may be changed by socialization experiences and aging over time. Brodkin and Weiss (1990) pointed out that young athletes who participated in sport looked more for social recognition than the middle-aged and older adults. Similarly, Duda and Tappe (1988) reported there was a decrease in competition objectives from younger to older men, if the exercise program became too challenging and they needed to perform with great physical competence. Older adults chose not to participate if the competence level rose too much. Thus, Kleiber and Kelly (1980) summarized that there was a movement away from an ego orientation in the middle-aged and the older adults, thus the middle-aged and the older adults would not be interested in physically demanding recreation activities.
### Method
#### Participants
A total of 160 participants (108 men and 52 women) at the Hong Kong Master Games, were invited to participate in this study. The participants were from 35 to 77 years old (M= 46.15 years old, SD= 9.2). They were divided into three age groups (30-39 years old, n=32; 40-49 years old, n=96; above 50 years old, n=32). Convenient sampling method was used and the selected eight events were: tennis, orienteering, distance run, swimming, badminton, squash, lawn bowls and gate ball.
#### Instruments
The measuring instruments used for the study were the _Participation Motivation Inventory_ (Gill, Gross & Huddleston, 1983) and _Task and Ego Orientation in Sport Questionnaire_ (Duda & Whitehead, 1998). The questionnaire was divided into three sections. The first part was the participation motives. The second part was the task and ego orientation; while the third part was the personal information, such as the frequency and duration of practicing.
The participants were requested to choose the most appropriate response that could best describe their personal feelings based on a 5-point Likert Scales. There were 30 items in the _Participation Motivation Inventory_. Participants responded to the statement: “I participated in the Master Games because …” by indicating their preferences from 1 (Very unimportant) to 5 (Very important). The scale revealed eight motivational factors: fun, achievement/status, team atmosphere, fitness, energy release, skill development, affiliation/ friendship and such miscellaneous motives as participation motives for sport and physical activity. The _Task and Ego Orientation in the Sport Questionnaire_ (TEOSQ) consisted of 13 items. The responses ranked the statement “I feel most successful in sports when …” from 1(strongly disagree) to 5(strongly agree). There were 7 items on task orientation and 6 items on ego orientation on the TEOSQ.
### Results
#### Participation Motives
The _Participation Motivation Inventory_ could be categorized into eight participation motives. The rank order of participation motive scores from the highest to the lowest were the following: Fun (M = 4.35, SD = 0.53 ); Affiliation /Friendship (M= 4.11, SD = 0.65); Fitness (M= 4.00, SD = 0.69); Skill development (M= 3.96, SD = 0.72); Achievement/ Status (M = 3.70, SD = 0.70); Team atmosphere (M = 3.58, SD = 0.99); Energy release (M = 3.36, SD = 0.74) and Miscellaneous (M = 2.99, SD = 0.82).
##### Gender
As the number of participant per cell was too small to conduct the 2 x 3 factorial design, two individual Multiple Analysis of Variance (MANOVA) were utilized to compare the mean vectors of the eight participation motives. The Wilks’ Lambda value for gender was not significant (p > .05) which revealed that the participation motives for men and women were similar. The means and standard deviations of the eight participation motives for men and women are listed in Table 1.
##### Age Group
The Wilks’ Lambda value for the age group was significant (p < .05). The discriminant functions obtained for the eight participation motives were significant.
Moreover, the oldest age group had significantly higher scores on skill development F(2, 157) = 3.4, p =.036; achievement/ status F(2, 157) =11.12, p =.000; team atmosphere F(2, 157) =9.18, p = .000; fitness F(2, 157) = 8.81, p = .000; energy release F(2, 157) =10.97, p = .000; skill development F(2, 157) =6.54, p = .002; affiliation/friendship F(2, 157) = 13.31, p = .000 and miscellaneous F(2, 157) = 9.68, p = .000. Post Hoc Tukey Tests were utilized and the results reported that the participants aged over 50 years had significantly higher scores than the participants aged 30 to 39 years for the seven participation motives except skill development. They also had significantly higher scores than the participants aged 40 to 49 years for seven participation motives except fun. The participation motives for the 30 to 39 age group and the 40 to 49 age group were similar. The means and standard deviations of the eight participation motives for three age groups are listed in Table 2.
##### Experience
There were 33 participants took part in this event for the first time and 127 of them had participated in this event before. The Wilks’ Lambda value for experience was not significant (p > .05) revealed that the participation motives for participants with different levels of experience in the Master Game were similar
#### Task and Ego Orientations
There were 13 items in the _Task and Ego Orientations in Sport Questionnaire_ and the top three goal orientation statements were “do my very best”; “something I learn makes me want to go and practice more”; and “work really hard”. Details are listed in tabled 3. The goal orientations of all participants in the Master Games was task orientation (M=4.02, SD = 0.51) rather than ego orientation (M=3.43, SD = 0.75).
##### Gender
The Wilks’ Lambda value for gender was not significant (p > .05) which revealed that the goal orientation scores for men and women were similar and the means and standard deviations of the eight participation motives for men and women are listed in Table 4.
##### Age Group
The Wilks’ Lambda value for the age group was significant (p < .05). The discriminant functions obtained for both ego and task orientations were significant.
Ego orientation, F(2, 157) = 4.09, p = .019; Task orientation, F(2, 157) =3.34, p = .038. The Tukey tests indicated that the oldest participants (aged over 50 years) had significantly higher mean ego orientation score than the youngest participants (aged 30-39 years). They also had significantly higher mean task orientation score than the 40-49 years old group.
##### Experience
The Wilks’ Lambda value for the experience group was significant (p < .05). The discriminant functions obtained for ego orientation was significant, F(1, 158) =14.08, p = .000. The means and standard deviations of the ego orientation score for the no experience group was M = 3.00, SD = .71; and the previous experience group was M = 3.54, SD =.73. The task orientation score for participants without and with previous experience was similar.
### Discussion
This study is concerned with participation motives and goal orientations of individuals participating in the Master Games. After comparing the eight dimensions of participation motives, fun and affiliation/friendship are the most influential motivators that encouraged individuals to take part in the Master Games. For the goal orientations of sport participation, most participants take part in physical activities to meet their task orientation needs.
This study supported previous research that participation motives for men and women were similar and having fun was an important motive (Shapiro, 2003).
The oldest adults (ages over 50 years) had the highest scores on most of the participation motives and they ranked “Affliation/Friendship” , “fun” and “fitness” as the top three motives. This supports previous research which indicated that older adult participated in physical activity for psychological and social purposes (Rudman,1989).
There were significant mean differences on goal orientation scores among the three age groups. Participants with ages above 50 years old had higher scores on ego orientation than participants between the 30 to 39 years old. This situation may be due to the fact that the older participants have more years of experience in the Master Games, thus they had more confidence in their ability as compared with the others.
Furthermore, the result on the task orientation score reflected that all participants would like to master their skill and they believed that success in competition would depend on practicing the skill and their effort. This finding does not support the finding of Steinberg, Grieve and Glass (2002) which stated that the ego orientation score for the over 50 years old male group was lower than the younger groups. This difference could be due to a cultural intervening variable, to be more precise, since Chinese culture assigns greater respect to the “elders” than Western culture, this finding was not unexpected.
#### Experience Difference on Participation Motives and Goal Orientations
Previous participation experience was one of the important factors which determined whether an individual would master a new skill and their attitudes towards the Master Games. The participation motives for people with different previous experience in the Master Games were similar.
For task orientation, no significant difference was found in the participation experience. On the other hand, participants with previous experience have significantly higher scores on the ego orientation than individuals without previous experience. The results supported that participation experience could enhance participants’ confidence in competition and they would like to out perform others. In other words, participation satisfaction socializes the participants into seeking more participation in order to gain more satisfaction and the positive cycle keeps repeating itself and it eventually becomes a self-fulfilling prophecy.
### Conclusion
In this study, the participation motives and goal orientations of men and women are similar. Older adults have higher mean score on the following seven motivational factors (“Fun”, “Achievement/ Status”, “Team atmosphere’, “Fitness”, “Energy release”, “Affiliation/Friendship” and “Miscellaneous”) than the youngest adults. Fun is an important motive for all participants.
For goal orientations, older participants have higher mean scores on ego orientation. The participation motives and task orientation score for participants with different experience are similar. Participants with previous experience have a higher ego orientation score than those without previous experience.
The application of this study to the world of Masters Sport and “leisure Sports” as well as “Serious Leisure” is very salient. As the Post-Industrial world “ages”, there will be a greater need for “leisure sports” whose main goal is “Health Promotion”.If leisure sports contribute to both a positive ego-enhancing psychological and physical outcome, then this will greatly reduce the pressure on the health care system in post-industrial medically oriented societies. The provision of professionally planned leisure sports for seniors is far more financially economic than the need for more long-term care and pharmaceutical solutions to caring for our aging populations. Greater emphasis needs to be placed in the development of curricula that addresses the growing need to educate future leaders in the delivery of leisure sports ranging from low-intensity activities such as walking, swimming, biking and skiing to more highly organized leisure sports that may be viewed as more “serious” forms of leisure sports (Stebbins, 2007) that require long-term training and professional coaching.
#### Recommendations for Future Studies
The sample size of the research should be larger, cross-cultural with more qualitative grounded research methods so that the study could be more representative and generalizable. In addition, cross-cultural case studies should be developed to gather more information on participation motives and goal orientations as impacted by different cultural and socialization patterns.
Most Western societies see a significant ageing of their population that will be further accentuated in the coming decades. Future research should carry out cost-benefit analysis of the value of Master Sports and Leisure Sports on reducing the medical costs of an ageing population that can maintain their “independence” as a result of these activities. The ability of older adults to function independently depends largely on maintenance of aerobic capacity and muscle strength. Furthermore some longitudinal studies suggest that physical activity is linked to a reduced risk of developing Dementia and Alzheimer’s disease.
Furthermore, links between theoretical model building and policy and management strategies need to be nurtured as there presently exists a disconnection between the two. It is recommended that all Masters Games should include a research and evaluation component for the betterment of the games and our ageing society.
### Tables
#### Table 1
Means and Standard Deviations of eight motives for men and women.
| Sources | Men (n = 108) | Women (n = 52) | ||
|---|---|---|---|---|
| M | SD | M | SD | |
| Fun | 4.42 | 0.51 | 4.21 | 0.55 |
| Affiliation / Friendship | 4.17 | 0.63 | 3.98 | 0.68 |
| Fitness | 4.01 | 0.71 | 3.95 | 0.65 |
| Skill Development | 3.97 | 0.74 | 3.95 | 0.68 |
| Achievement / Status | 3.77 | 0.69 | 3.54 | 0.72 |
| Team atmosphere | 3.56 | 1.1 | 3.63 | 0.84 |
| Energy release | 3.38 | 0.76 | 3.32 | 0.71 |
| Miscellaneous | 2.98 | 0.82 | 3.01 | 0.81 |
#### Table 2
Means and Standard Deviations of eight motives for participants in three age groups.
| Sources | Age 30-39 (n = 32) | Age 40-49 (n = 96) | Age over 50 (n = 32) | |||
|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | |
| Fun | 4.25 | 0.63 | 4.32 | 0.51 | 4.56 | 0.45 |
| Affiliation / Friendship | 4.15 | 0.72 | 3.94 | 0.61 | 4.57 | 0.43 |
| Fitness | 3.75 | 0.87 | 3.93 | 0.64 | 4.41 | 0.46 |
| Skill Development | 3.97 | 0.94 | 3.83 | 0.62 | 4.34 | 0.60 |
| Achievement / Status | 3.51 | 0.61 | 3.60 | 0.69 | 4.19 | 0.61 |
| Team atmosphere | 3.40 | 1.09 | 3.43 | 0.92 | 4.22 | 0.87 |
| Energy release | 3.21 | 0.74 | 3.24 | 0.73 | 3.88 | 0.52 |
| Miscellaneous | 2.86 | 0.76 | 2.85 | 0.79 | 3.53 | 0.74 |
#### Table 3
Rank Order on goal orientations for participants in the Master Games (N=160).
| Rank Order | Items | M | SD |
|---|---|---|---|
| 1 | I do my very best | 4.38 | 0.65 |
| 2 | Something I learn makes me want to go and practice more | 4.11 | 0.62 |
| 3 | I work really hard | 4.09 | 0.72 |
| 4 | A skill I learn really feels right | 3.98 | 0.68 |
| 5 | I learn a new skill by trying hard | 3.95 | 0.74 |
| 6 | I learn something that is fun to do | 3.83 | 0.71 |
| 7 | I learn a new skill and it makes me want to practice more | 3.82 | 0.79 |
| 8 | I’m the best | 3.69 | 1.05 |
| 9 | I score the most points / goals / hits, etc. | 3.53 | 0.88 |
| 10 | I can do better than my friends | 3.44 | 0.95 |
| 11 | Others mess-up “and” I don’t | 3.41 | 0.99 |
| 12 | The others can’t do as well as me | 3.33 | 1.02 |
| 13 | I’m the only one who can do the play or skill | 3.16 | 1.04 |
#### Table 4
Means and Standard Deviations of goal orientations for men and women.
| Sources | Men (n = 108) | Women (n = 52) | ||
|---|---|---|---|---|
| M | SD | M | SD | |
| Ego | 3.45 | 0.74 | 3.38 | 0.79 |
| Task | 4.04 | 0.52 | 3.98 | 0.49 |
#### Table 5
Means and Standard Deviations of goal orientations for participants in three age groups.
| Sources | Age 30-39 (n = 32) | Age 40-49 (n = 96) | Age over 50 (n = 32) | |||
|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | |
| Ego | 3.20 | 0.74 | 3.40 | 0.79 | 3.72 | 0.58 |
| Task | 4.11 | 0.61 | 3.94 | 0.45 | 4.18 | 0.53 |
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### Corresponding Author
Prof. Siu Yin Cheung
Department of Physical Education, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
Telephone: (852) 3411-5637
Fax: (852) 3411-5757
E-Mail: <cheungsy@hkbu.edu.hk>
A Study of Golfers in Tennessee
### Abstract
The purpose of this study was to investigate preferred shopping behaviors of golfers in the state of Tennessee. While much research has been done on retail shopping behavior in general, little exists regarding shopping behavior in sport retail, and more specifically golf retail. While golfer behavior has been researched in other areas such as tourism, it has not been fully researched in the sport or retail literature. Since this segment of consumer spends millions of dollars per year, this study was conducted to fill the gap in the literature regarding this unique consumer. An online survey was distributed among a state-wide professional golf organization regarding preferred shopping and golf course attributes. Results showed a significant relationship between some variables, including brands/designers offered. This research will be helpful to golf retailers, golf merchandisers, golf marketers and managers, who sell, buy or deal with golf apparel and/or related merchandise to better tailor marketing and promotional activities and ultimately increase revenue. This paper is unique and applicable in the fact that golf has not been fully researched in the marketing or retail area.
**Key words:** golf, marketing, consumer behavior, retail
### Introduction
Sport and leisure have been researched in many capacities over many years. Topics encompass marketing (42), travel style (40), satisfaction (49), retail (12), behavior (72) religion (65), gender-based (38), product involvement (6), sport (74) and many others that have been analyzed to better understand this phenomena. Understanding sport and leisure and its many facets are important not only to extend retail-based research, but to present possible opportunities to uncover more about some of the still underdeveloped theories of retail and consumer behavior within this area. It has been shown that consumers will spend significant amounts of money on leisure (28). Consumer shopping behavior has been proven to be important and relevant in other industries such as the tourism industry (50, 11).
Due to the significant nature of money spent on sport and leisure by consumers, sport marketers, merchandisers and others realize the need to segment the different types of sport consumers. Some studies have addressed and studied the specialized segmentation of the sport consumer. Not only do sport consumers hold specific values and attitudes (46), but they require marketers, retailers and others to take note of their unique spending habits. Other traditional consumer behavior concepts apply to the sport consumer such as brand loyalty (8), emotional attachment (67), and brand equity (20).
#### Golf Industry
Because the sport consumer holds some of the same behavioral traits as traditional consumers, it is important to investigate the behaviors of the sport consumer in more detail. Many sports have been investigated in regard to its consumer such as the brand loyalty of baseball, wrestling (32) and football (41). To continue to investigate the sport consumer, this paper will attempt to identify golfer consumer-based behaviors. This may help all stakeholders, to include retailers, merchandisers, academics and golf managers to better understand, serve and recognize golfer segments and to determine segmentation and/or marketing strategy for applicable segments. Though this type of study has been conducted for other entities (professional golfing organizations, for example), it has not been conducted in this manner, thus adding to the small current body of literature in this area of retail study.
Participating in a sport while partaking of a leisure activity, such as a vacation, has been found to be a growing occurrence (27). Further, one activity that has received some attention is the golfing industry. Golf’s popularity continues to increase with as estimated 28.6 million participants as of 2009 (48). In fact, in 2008, golf generated approximately $76 billion in goods and services (21). Another report indicated that golfers spent $4.7 billion on equipment alone and $19.7 billion on green fees in 2002 (22). But, surprisingly, golf has been noted to be an under-researched activity (14), especially considering the impact it can make to the local and state economy. Golf travel, tourism, facility management and golf-related real estate (73) are a few of the important areas of the golf industry. It has also been estimated that the average dollar amount spent per person per golf trip was $452 with an almost 40 million golf trips taken (64). In addition, golfers spent $26.1 billion a year on golf travel (22). Research has been conducted to learn about different aspects of the sport. Topics that have been studied have included golfer’s satisfaction (53, 54) destination choice (27, 14, 34), golf course development (69) and seasonality (18). Golfing lifestyles have also been a focus of research inquiry. One study found four distinct tourist typologies within the golfing industry which were: quality-seeker, competitor, high-income and value-seeker. These typologies were chosen using many attributes and demographics such as course layout, availability of tee times, fees, income, gender and age (70). A recent article even investigated the willingness of golfers to pay for a higher environmental quality of the golf course (37). Other research has focused up on the economic impact of golf to include pricing (63, 47, 39). More specifically, several studies have been conducted that focused upon individual states and the economic impact of golf. For example, the golf industry in Florida (25), South Carolina (17), Arizona (58), Oklahoma (59) and Georgia (13) have all been studied and each revealed a significant impact to the state economy. One study indicated the economic impact of golf in Tennessee was significant. With over 200 golf courses in Tennessee, the golf industry directly employed over 5,000 people, with annual wages estimated at $97 million and a direct economic impact of over $313 million (26).
Golf is a sport that has been subject to study in regard to segmentation and thus marketing strategy. Petrick (53) found that several different segments of golfers exist by examining past behavior and experience level. Differences were found, too, in perceived value, satisfaction and intention to revisit. Golfers have also been segmented by spending habits, with heavy spenders being especially transparent in their habits (60). Another recent study found that certain segments of golfers tend to pay attention to different store attributes such as cleanliness and store appearance (36). Even length of stay in regard to the golf traveler has been noted to be of significance when analyzing different segments of golfers (4). Image of the golf destination was found to be different among different golfer segments (51). Therefore, it is important to continue to study golfers and how different segments of golfers consume and behave because the shopping behavior of consumers can impact profitability and revenue of many facets of the golf retail industry.
#### Shopping attributes and involvement
The concept and theory of involvement has long been studied and analyzed in numerous areas of research and has been proven to be connected to shopping behavior. It has been found to be important in many ways to include web site design (75), persuasion (33), and product experience (5). Product involvement in such areas as leisure studies has been described even more specifically by being termed enduring involvement which is the “the central notion is that of an abiding interest in, and attachment to, a product class which is independent of purchase or other situational factors” and has been found to be linked to leisure in three main ways: enthusiasm, experience and satisfaction. Product enthusiasm connects the consumer with the leisure activity and the products associated with the activity which transcends most one-time purchases which has been the bulk of most research regarding involvement (6). Therefore, studying golfers and their enduring involvement with golf-related products and services are important. Golfers may become involved with numerous products such as equipment, facilities, shopping behaviors, particular brands or store attributes. Enduring involvement has also been correlated to participation in the activity or product (45) and has been found to have a relationship with situational involvement (57). Enduring involvement has also been studied specifically in the golf environment. It was found that enduring involvement (activity, length of participation, attraction and risk consequence) had a positive relationship with length of participation when studied with the variable of seasonality (24). In addition, involvement has been shown to have a predictive power in regard to usage of the product (52). Involvement was also found to be important in the golf environment when determining level of involvement, the psychological commitment to a brand and attitudinal characteristics (30, 31). Golf has been studied with enduring involvement with the attribute of gender. It found that women are involved with golf for different reasons than men to include purpose, leisure entitlement and status (43).
A main variable that may influence a customer of sporting activity are store attributes. Many studies have shown that store attributes such as pricing (62, 23), atmospherics (55), product/brand selection (61), quality (9), salespeople (19), convenience (16), location (15), and image (29) all influence purchase behavior in some manner. One study found that people, who are involved in a particular sport activity every day, will most likely participate in that same activity while on a vacation (7). In addition, product involvement has been positively associated with leisure in regard to sporting activities. For example, product involvement and leisure have been shown to have a relationship in such sporting activities such as biking (68), yoga (10), boating (35) basketball (1) golf (44) and skiing (2).
However, one area ripe for development in leisure study is the consumer’s involvement and shopping behavior in regard to the consumer’s chosen sport activity. Further, one leisure activity that has shown evidence of growth and importance in regard to consumer involvement and shopping behavior is golf. It is important to understand the different types of golfers and how they behave for several reasons. First, the golfer market is a significant one since golfers worldwide number in the millions. Further, within those millions, different segments exist (53). Therefore, understanding those separate segments is important to determine leisure, marketing, retailing or other business strategy. For example, different golfer segments may be segmented by frequency of play, shopping behavior or purchase behavior. Since so little is known about different golfer segments, it is important to study these golfers and learn how to better serve them. Learning more about golfer segments will encourage, increase and generate revenue which will ultimately be beneficial to the golf retail industry, golf merchandisers and golf managers.
#### Conceptual Framework
Based on the existing literature and the lack of it in regard to combination of the variables given of store attributes, involvement and golf, an exploratory conceptual framework is offered. The following conceptual framework is posited to attempt to explain how sporting activities, such as golf, may be impacted based on involvement, specific store attributes and the patronage/re-patronage of products that may be associated with golf. This model begins by suggesting that the golf consumer’s involvement commences with a golf product or service. Thus, after becoming involved with the sport, the consumer will engage and become further involved with golf-related attributes. These attributes may be such items as the golf course itself (design, condition), the facility (pro shop, practice) staff and facility product offerings such as apparel, hard goods or availability of lessons. Because of a golfer’s proven connection with the different attributes of golfing products/services, patronage is likely to occur. Further, since golfers have been proven to be psychologically connected to a brand, it is suggested that this involvement with the golf-related attributes of the product or service, will transcend into usage or patronage of the product or service.
#### Research Objectives
While attempting to develop a business strategy for a golf retailer, golf course or destination, many variables, such as store image, cleanliness of the store, friendliness of the salespeople, frequency of play, course design or course location, must be considered. Just as any traditional retail establishment utilizes segmentation techniques to tailor their marketing to a particular target market, golf retailers and destinations in Tennessee may also like to use these techniques. Through all golf literature, little research exists regarding the analysis of golfer shopping behavior and consumption patterns. Therefore, the purposes of this study are to:
* Segment the golfing population in Tennessee to categorize golfers by shopping behavior characteristics and preferred golf course attributes.
* Present a competitive advantage strategy for golf courses regarding golfers’ shopping behavior and preferred golf course attributes in Tennessee.
* Assess the potential benefit to the relevant stakeholders of promoting golfing based on shopping behavior and preferred golf course attributes in Tennessee.
### Methods
The data were collected via an online survey as distributed by a statewide golf association in Tennessee on behalf of the researchers. The online survey was adapted from a tested and valid survey (70). The survey was pre-tested before distribution to a convenient sample of male and female golfers of all ages and resulted in no refinements.
The online survey was sent to every registered member of the golf association in the state of Tennessee. Approximately 15,000 surveys were distributed with 1,123 returned, yielding a return rate of 7.5%. Each golfer who completed the survey was given the opportunity at the end of the online survey to register for one of two $100 Visa gift cards. The participants were asked to give an email address where they could be reached if they were randomly chosen the winner. However, to maintain anonymity, the email address was given to the golf association, where the participant was then contacted by the association and not the researcher. The winners were chosen randomly using Research Randomizer (56). The data collection lasted six weeks with one reminder email sent from the golf association at the halfway point.
The questions were divided into three major sections including shopping behavior characteristics, preferred golf course attributes and demographic information. The first section asked participants, in ordinal scale format, how important particular attributes were when shopping for golf apparel and merchandise. Attributes questioned were store’s physical design and appearance, overall positive store image and reputation, and offers some type of “experience” beyond just shopping and others. Other shopping behavior questions asked about the participant’s preferred location to shop for golf merchandise and how much they spend on golf clothing and golf footwear. The second major section of the online survey consisted of preferred golf course attributes. Again, the participant was asked, in ordinal scale format, how important certain golf course/destination attributes were to them, personally. Some of the attributes on the online survey were course design, location, type of facility, discounts available and many others. Other questions were then asked regarding golf behaviors such as with whom the participant plays most often, average score, golf trips taken per year and others. The final section of the survey asked basic demographic information such as gender, age, income and zip code.
### Results
#### Participants
Demographic information was collected from 305 survey participants (due to an online survey glitch, not all participants were provided with the demographic questions). The responding participants were 88% male. The most common age range as well as the median was 50 to 59 (32%). For the 272 who reported their annual household income, the most common response was 37% indicating an income over $200,000 followed by 35% indicating it was $100,000-$199,999. The income result is reflective of other studies (71, 66) and may accurately represent the population in this study.
#### Frequencies
Due to the exploratory nature of this research, it was important to begin with frequency analysis of the behavioral questions which were survey questions one through twelve. The first question asked about ten attributes regarding shopping behavior of the participant. Knowledgeable salespeople were ranked the most important attribute followed by brands/designers offered. (Table 1.)
Question two asked the respondent to state where they mainly purchase golf merchandise. Pro shops and golf specialty stores were the main choices for purchasing golf-related merchandise. (See Table 2.)
Questions three and four asked how much the participant spends per year on golf apparel and footwear. The results showed that forty six percent (46%) of respondents spend over $250 per year on golfing apparel. Almost thirty-three percent of respondents (32.8%) answered that they spend between $101 – $150 on footwear yearly.
Question five was formatted much the same as question one. However, the main focus of this question asked not about shopping attributes, but golf course attributes and how important those attributes were when choosing where to play. The question asked about sixteen different attributes as shown in Table 3 which indicated course conditions and speed of play were ranked the highest.
The remaining behavioral questions (6-12) asked about particular behaviors of the golfers in regard to different specific important golfer attributes. Table 4 shows the most popular answer for each question which indicated the respondents tend to play with friends, play 8 or more times per month, mostly in Tennessee and at the same course.
#### Crosstabulations
Several of the survey questions were examined further to see if they were related. First, average score was examined in relationship to how much was spent on golf-related clothing and footwear. Both were significantly associated, with those having better scores spending more as shown in Table 5 and Table 6.
Question 10 (score) was also associated with responses to Question 5 (Please mark how important the following items would be when deciding where to play golf in Tennessee: course design). Those with better scores reported that course design was more important than other participants as shown in Table 7.
Fourth, Question 10 (score) was associated with Question 1 (When deciding on a place to shop for golf apparel and merchandise, how important are each of the following factors: well-known brands or designer products are offered). Those with better scores thought brands and designers offered were more important. (See Table 8).
Finally, Question 3 (How much do you spend in an average year for golf clothing?) was associated with Question 1 (When deciding on a place to shop for golf apparel and merchandise, how important are each of the following factors: well-known brands or designer products are offered). Those participants that spent $201 or more on golf clothing were more likely to indicate brands or designs offered were important or very important than were other participants.
### Tables
#### Table 1
Responses to Ten Ordinal Scale Statements Regarding Shopping Behavior Attributes
When deciding on a place to shop for golf apparel and merchandise, how important are each of the following factors?
| Very Important 5 |
Important 4 |
Neutral f(%) 3 |
Unimportant 2 |
Very Unimportant 1 |
Median | |
|---|---|---|---|---|---|---|
| Store’s physical design and appearance | 65 (6) | 509 (45) | 392 (35) | 112 (10) | 45 (4) | 4 |
| Well-known brands or designer products are offered | 393 (35) | 548 (49) | 112 (10) | 40 (4) | 30 (3) | 4 |
| Store specializes in golf products only | 150 (13) | 382 (34) | 395 (35) | 157 (14) | 33 (3) | 3 |
| Neatness and cleanliness of the store interior | 317 (28) | 636 (57) | 126 (11) | 15 (1) | 22 (2) | 4 |
| Overall positive store image and reputation | 298 (27) | 682 (61) | 104 (9) | 19 (2) | 19 (2) | 4 |
| Accessibility and parking | 163 (15) | 574 (51) | 311 (28) | 52 (5) | 18 (2) | 4 |
| Days and hours open for shopping | 175 (16) | 611 (55) | 262 (24) | 40 (4) | 25 (2) | 4 |
| Offers some type of ‘experience’ beyond just shopping | 125 (11) | 340 (30) | 375 (34) | 201 (18) | 78 (7) | 3 |
| Attitude and enthusiasm of salespeople | 321 (29) | 555 (50) | 177 (16) | 36 (3) | 27 (2) | 4 |
| Knowledgeable salespeople | 549 (49) | 444 (40) | 69 (6) | 19 (2) | 31 (3) | 4 |
Items may not total 100 due to rounding errors
#### Table 2
Responses to Statements Regarding Where Participants Shop for Golf Merchandise
| Purchase Location | Percentage |
|---|---|
| Pro shop | 59 |
| General sporting goods store | 25 |
| Discount | 3 |
| Golf specialty store | 37 |
| Online | 27 |
| Other | 8 |
#### Table 3
Responses to Sixteen Ordinal Scale Statements Regarding Golf Course Attributes
| Very Important 5 |
Important 4 |
Neutral f(%) 3 |
Unimportant 2 |
Very Unimportant 1 |
Median | |
|---|---|---|---|---|---|---|
| Condition of fairway and greens | 623 (56) | 462 (41) | 14 (1) | 3 (3) | 21 (2) | 5 |
| Course ambience | 157 (14) | 742 (66) | 193 (17) | 19 (2) | 12 (1) | 4 |
| Course design | 228 (20) | 700 (62) | 162 (14) | 23 (2) | 11 (1) | 4 |
| Price/Fees | 283 (25) | 542 (48) | 233 (21) | 43 (4) | 20 (2) | 4 |
| Practice facility | 133 (12) | 464 (41) | 397 (35) | 99 (9) | 28 (3) | 4 |
| Speed of play | 397 (35) | 559 (50) | 131 (12) | 19 (2) | 17 (2) | 4 |
| Tee time availability | 306 (27) | 649 (58) | 130 (12) | 11 (1) | 20 (2) | 4 |
| Location | 229 (21) | 625 (56) | 217 (20) | 27 (2) | 16 (1) | 4 |
| Type of facility (municipal, resort, etc.) | 82 (7) | 342 (31) | 530 (48) | 105 (10) | 51 (5) | 3 |
| Staff (salespeople, golf pros) | 99 (9) | 452 (41) | 412 (37) | 122 (11) | 31 (3) | 4 |
| Availability of lessons or clinics | 21 (2) | 78 (7) | 415 (37) | 373 (34) | 226 (20) | 3 |
| If you are a member of the course or not | 159 (14) | 281 (25) | 393 (36) | 185 (17) | 89 (8) | 3 |
| Availability of GPS system on course or cart | 33 (3) | 147 (13) | 416 (37) | 292 (26) | 227 (20) | 3 |
| Choice to walk or ride | 165 (15) | 335 (30) | 394 (35) | 134 (12) | 84 (8) | 3 |
| Discounts available (such as TPGA PassKey or GolfNow.com) | 55 (5) | 261 (23) | 477 (43) | 202 (18) | 119 (11) | 3 |
| Pro shop merchandise | 21 (2) | 213 (19) | 513 (46) | 223 (20) | 144 (13) | 3 |
Items may not total 100 due to rounding errors
#### Table 4
Responses to Statements Regarding Golfer Behavior Attributes
| Golfer attribute | Most popular answer | Percentage of most popular answer |
|---|---|---|
| Who the golfer plays with the most | Friends | 84 |
| How many rounds played per month | 8 and over | 53 |
| How many played in Tennessee | Most | 71 |
| How many played at the same course | Most | 69 |
| Average 18 hole score | 7-12 over par | 39 |
| Golf trips taken per year (overnight) | 0-2 | 61 |
| People in residence who play golf | 1 | 50 |
#### Table 5
Relationship Between Score and Amount Spent on Clothing
Score and amount spent on clothing
| 0-49 | 50-100 | 101-150 f(%) | 151-200 | 201-249 | Over 250 | |
|---|---|---|---|---|---|---|
| Par to 6 over | 1 (.5) | 9 (4) | 21 (10) | 32 (15) | 27 (13) | 123 (58) |
| 7 to 12 | 3 (.7) | 29 (7) | 42 (10) | 81 (19) | 73 (17) | 197 (46) |
| 13 to 18 | 3 (.9) | 26 (8) | 44 (14) | 70 (22) | 47 (15) | 129 (40) |
| 19 or above | 0 (0) | 13 (9) | 26 (17) | 31 (21) | 24 (16) | 57 (38) |
Chi-square = 27.929; p = .022
Items may not total 100 due to rounding errors
#### Table 6
Relationship Between Score and Amount Spent on Footwear
Score and amount spent on footwear
| 0-49 | 50-100 | 101-150 f(%) | 151-200 | 201-249 | Over 250 | |
|---|---|---|---|---|---|---|
| Par to 6 over | 7 (3) | 33 (16) | 60 (28) | 37 (17) | 44 (21) | 32 (15) |
| 7 to 12 | 20 (5) | 86 (20) | 147 (34) | 100 (23) | 50 (12) | 25 (6) |
| 13 to 18 | 28 (9) | 83 (26) | 113 (35) | 61 (19) | 17 (5) | 17 (5) |
| 19 or above | 10 (7) | 50 (33) | 47 (31) | 25 (16) | 13 (9) | 7 (5) |
Chi-square = 79.542; p = .000
Items may not total 100 due to rounding errors
#### Table 7
Relationship Between Score and Course Design
Score and course design
| Very Unimportant 5 |
Unimportant 4 |
Neutral f(%) 3 |
Important 2 |
Very Important 1 |
|
|---|---|---|---|---|---|
| Par to 6 over | 2(.9) | 2 (.9) | 21 (10) | 130 (61) | 58 (27) |
| 7 to 12 | 3 (.7) | 8 (2) | 49 (11) | 273 (64) | 96 (22) |
| 13 to 18 | 4 (1) | 9 (3) | 55 (17) | 199 (62) | 52 (16) |
| 19 or above | 2 (1) | 4 (3) | 37 (24) | 92 (61) | 17 (11) |
Chi-square = 36.070; p = .000
Items may not total 100 due to rounding errors
#### Table 8
Relationship Between Score and Brands/Designers Offered
Score and brands/designers offered
| Very Unimportant 5 |
Unimportant 4 |
Neutral f(%) 3 |
Important 2 |
Very Important 1 |
|
|---|---|---|---|---|---|
| Par to 6 over | 6 (3) | 3 (1) | 12 (6) | 83 (39) | 109 (51) |
| 7 to 12 | 12 (3) | 15 (4) | 34 (8) | 212 (50) | 155 (36) |
| 13 to 18 | 9 (3) | 12 (4) | 40 (13) | 164 (51) | 94 (30) |
| 19 or above | 3 (2) | 9 (6) | 26 (17) | 86 (57) | 28 (18) |
Chi-square = 58.700; p = .000
Items may not total 100 due to rounding errors
#### Table 9
Relationship Between Amount Spent on Clothing and Brand/Designers Offered
Amount spent on clothing and brands/designers offered
| Very Unimportant 5 |
Unimportant 4 |
Neutral f(%) 3 |
Important 2 |
Very Important 1 |
|
|---|---|---|---|---|---|
| 0-49 | 0 (0) | 3 (38) | 1 (13) | 0 (0) | 4 (50) |
| 50-100 | 3 (4) | 4 (5) | 15 (20) | 40 (52) | 15 (20) |
| 101-150 | 3 (2) | 9 (7) | 25 (19) | 73 (55) | 24 (18) |
| 151-200 | 8 (4) | 7 (3) | 22 (10) | 104 (49) | 72 (34) |
| 201-249 | 5 (3) | 6 (4) | 19 (11) | 76 (44) | 65 (38) |
| Over 250 | 10 (2) | 11 (2) | 29 (6) | 252 (49) | 213 (41) |
Chi-square = 92.079; p = .000
Items may not total 100 due to rounding errors
### Figures
#### Figure 1
#### Conclusion and Applications in Sport
There are several articles that have investigated the game of golf. Some have emphasized golf’s economic contributions on a regional or state level. Other research attempted to study the tourism and travel behaviors of golfers. However, this article has provided an overview of shopping behaviors of golfers specifically to the state of Tennessee. In addition, it has also attempted to identify golfer preferred shopping attributes, present possible competitive advantages and assess potential benefits to stakeholders in relation to golf course attributes in Tennessee. This research begins to identify shopping behaviors of golfers to aid in the attempt to better market to golfers and provide the golfing consumer with desired products and services.
Golf courses, golf pro shops, golf associations, such as the Association of Golf Merchandisers (3) and retail stores must develop strategies to better market to Tennessee residents (and other states and regions) who play golf. In the current study, several implications exist that may help golf managers, buyers and others who manage or sell golf products and services. First, it was found that knowledgeable salespeople were the most important attribute for a facility. Therefore, it may be important for managers to focus upon intense training of employees in regard to products and services offered. Since golf is typically a seasonal sport, employees may also be only hired for seasonal employment. This may be a problem since the employee may come and go faster than the management could train the employee. However, by training before heavy playing times, and continually training full-time staff (pros, greenskeepers, etc.), the staff can remain current in all golf trends. The second most important attribute, which was brands/designers offered could imply that the facility should research as to which brands are the most desired and/or to possibly increase brand choice. According to this survey, many golfers spend a considerable amount of money on golfing merchandise per year (almost half spent over $250 annually on apparel alone). Additionally, the literature and this study show that many golfers have a high income. Therefore, the opportunity to spend in the pro shop, where this survey reveals is where most golfers shop, has the potential to be a source of high revenue. Typically, local pro shops are small in square footage, therefore making every inch of floor space crucial. Thus, being aware of which brands are current (those seen in golfing magazines, what players are wearing on television, etc.) should be of utmost importance to managers, buyers, etc. It should be noted that the significant relationship between the amount spent on apparel/footwear and score, indicated that better scorers are willing to spend more than other players. Therefore, the manager/staff should be aware of their better scoring players and focus on them specifically by offering special promotions in which they most likely will participate.
Another important implication from this study emphasizes the importance of what attributes of a course to promote and market. According to results of this survey, course conditions and speed of play were ranked the highest in regard to course attributes. Therefore, any promotions in Tennessee should focus upon these attributes by emphasizing exemplary course conditions and course rules surrounding speed of play. Further, it was found that better scorers thought course design was most important on choosing where to play golf in Tennessee. By promoting course design (course designer, yardage, etc.) to better scoring golfers, revenue may be increased by attracting those golfers to the course. All of these strategies are highly tailored and personalized. However, these strategies adhere to current marketing trends of tailoring promotional activities to specific customers.
It is important to recognize how golfers behave in regard to shopping behaviors. Acknowledging and targeting these shoppers help managers know how to better manage their dollars in regard to marketing, determining product assortment or addition/deletion of services. Next, knowing what golfers buy is crucial to produce effective and profitable outcomes. In addition, managers should know what attributes golfers shop for when they shop for golfing goods and services. Lastly, identifying where golfers shop for merchandise and services is important for allocation and effective use of monies and resources. Knowing as much as possible about their customers will help in the construction of segmentation, targeting and customer service strategies.
It may be useful to replicate this study on a national level. One limitation of this study is that the sample did not encompass every golfer in Tennessee. However, golf is continuing to grow as a sport, a recreational activity and as tourism destinations (4). Therefore, golf is being recognized as a significant source of economic impact and revenue for local communities, states and regions. Further, additional research is needed to help retailers and other golf stakeholders not only in Tennessee, but other areas, to successfully market and sell golf products and services to potential and current consumers.
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### Corresponding Author
Kelly Price, Ph.D.
Assistant Professor of Marketing
East Tennessee State University
Department of Management and Marketing
P.O. Box 70625
Johnson City, TN 37614
(423) 439-4422
<pricekb@etsu.edu>
Kelly is an Assistant Professor of Marketing at East Tennessee State University. Her research consists of issues related to golf and consumer behavior. Her professional experience includes twelve years of retail management including golf management, buying and marketing.