Submitted by: Linda Garza, MS – Purdue University and Sally J. Ford, PhD – Texas Woman’s University
Abstract
An intervention strategy was developed, implemented, and evaluated that aimed at minimizing performance anxiety. The goal was to guide NCAA Division I softball athletes in using a breathing technique that, by contributing to the management of performance anxiety, would help each athlete reach full potential on the softball field. The strategy focused on the effects of the breathing technique on the participants’ heart rates, in relation to daily anxiety events; a heart rate monitor and anxiety logs were used to obtain data. All 4 of the athletes studied indicated improvement at various stages in the program. (more…)
Submitted by: David C. Wyld – Southeastern Louisiana University
Abstract
The sports memorabilia marketplace today is a multibillion-dollar, global market. However, it is fraught with hazards, due to the large percentage of counterfeit memorabilia, which some estimates peg at 90% of all items on the market. This article overviews the sports memorabilia market and the growing problem of counterfeit items. Then, it examines the prospect for radio frequency identification (RFID) to be used to provide a verifiable chain of custody for articles of sports memorabilia – from the point the item is signed through all subsequent transfers. The article concludes with an analysis of the implications of the introduction of such track and trace authentication technology into this fragmented marketplace and the benefits for all parties involved in sports collectibles.
Keywords: radio frequency identification, chain of custody, authentication, sports memorabilia
Introduction
Autograph seekers. They are a part of every professional – and often amateur – athlete’s life. They are a fixture at sports teams’ training camps, host hotels and stadiums, and anywhere these signature collectors know that athletes will have to pass through on their way to or from an event. They also are a part of the well-known athlete’s every move, as autograph seekers can make it uncomfortable, even impossible, for athletes and their families to enjoy a meal in public or a trip to an amusement park. Many of these autograph hunters are kids, looking to get that one autograph of the professional baseball or football star they admire–the one whose poster they have hanging over their bed. Some of the signature hounds are adults, looking to have literally any athlete they can find sign any team item such as a ball, a bat, a helmet, a jersey, a game program, or so forth, in order to turn an ordinary item into a collectible.
The motivation of many of these autograph seekers is indeed innocent, hoping to have a memento of their favorite athlete or sports team for their wall or mantle. The kid who admires his or her favorite sports star, whether it’s Tiger Woods, Brett Favre, Kobe Bryant, Alex Rodriguez, or David Beckham, can have a lasting memory not just from the signed item but from their brief encounter with a sports legend. All too often however, the motive for the autograph seeker is money. Indeed, the chance is there to cash-in on an athlete’s celebrity, and the players and their teams know it. The worst of the lot are grown-ups who hire children to seek out star’s autographs on a paid basis; they work on the premise that the “cute kid factor” might entice the sports star to stop and sign an item for a 9-year-old child that they wouldn’t for a 40-year-old man. As Baseball Hall of Famer Robin Yount commented, “There is money to be made out there on autographs, (and) you see more people doing it these days for that reason — the business end of it” (Olson, 2006, n.p.).
Yet, the real truth of the matter is that while a signed article can be a point of personal pride, even perhaps a family heirloom, the actual value of the item to knowledgeable sports memorabilia collectors is very limited. That is because of the need to provide verifiable proof of the autographed item’s authenticity. Yes, you may have been at the New Orleans Saints’ training camp in Jackson, Mississippi (as my sons and I were this past summer) and personally witnessed star running back Reggie Bush autograph a football. However, if you were to want to sell the ball, as opposed to displaying it on a shelf in your son’s room, there’s no irrefutable proof that could assure the first buyer, let alone subsequent buyers in the future, as to the validity of Bush’s signature. Not that this stops autograph seekers from trying day after day to get that elusive personalization of basketballs by LeBron James, footballs by Peyton Manning, baseballs by Derek Jeter, and item after item by a myriad of stars. So disruptive to athlete’s lives are some autograph hounds that teams today commonly limit access to their players, not just out of concern for their economic well-being but for their physical safety as well (Maske and Lee, 2007). And, some athletes, such as Michael Jordan, make it publicly known that they will not sign an autograph except through the special events (and often private signing days) for agencies they have contracted with to represent them in what has become an increasingly lucrative market for athletes, supplementing, or even exceeding, what they make on the field by simply signing their names (Johns, n.d.; Fisher, 2000).
The sports memorabilia market today is a global marketplace, estimated to generate revenues in excess of $5 billion annually (Friess, 2007). Items of sports memorabilia are sold in a variety of venues, including physical and online stores, shows and auctions, and in private sales (Smith, n.d.). Small, independent “mom and pop” sports memorabilia stores were once a staple of strip malls across America. According to industry observers, the number of such stores has plummeted from approximately 4,700 a decade ago to just over a thousand today (Keteyian, 2006). Much of this decline can be traced to the shifting of buying and selling sports memorabilia to eBay and other major online auction sites, much as has occurred with other collectibles, such as coins, stamps and antique items (National Auctioneers Association, 2008). However, the ease of access and widening of the marketplace has fostered an explosion of online memorabilia sales. One can see evidence of this by punching in any well-known athlete’s name on eBay, and whether you search for David Beckham, Muhammad Ali, Tiger Woods, or even a lesser known star, you will come-up with dozens, even hundreds, of autographed items up for sale at any given time.
However, the move to greater online sales has only worsened the problem with counterfeit sports memorabilia (Van Riper, 2007). Indeed, it is a market unlike any other, due to the giant presence of counterfeit items. In fact, one law enforcement official described the sports memorabilia market today as being “like the Wild, Wild West” (Keteyian, 2006). Market analyst Havoscope (2008) has concluded that over half of the sports memorabilia market is comprised of counterfeit items. The official estimate from the Federal Bureau of Investigation (FBI) is that 70% of all signed sports collectibles on the market in the U.S. are counterfeit (Fisher, 2000); forged signatures on items which themselves may or may not be what they are purported to be (after all, even official merchandise from sports leagues and special events, such as Super Bowls, World Cups, or World Series, can be faked). Industry observers believe the true figure to be even higher, ranging to upwards of 90% of all sports collectibles (Prova Group, 2006)! Thus, this is perhaps the ultimate example of a caveat emptor (buyer beware) market.
Anyone can buy a piece of sports memorabilia to hang on the wall or show in a display case, and, if you’re happy with the price you paid for it, all the better. However, unless you personally witnessed the athlete signing the football or the baseball bat, the odds are that the item is not worth any more than what you would have paid for an unsigned version at a local sporting goods store. Thus, there is a great need to have a solution that can assure buyers and sellers of the authenticity of an item, not just presently, but into the future. As we will examine, the certification process today itself is problematic and only contributes to the problem.
For the first time, the advent of radio frequency identification (RFID) technology provides an opportunity for the sports memorabilia marketplace to have the ability for buyers and sellers alike to rely upon a readily accessible and verifiable “chain of custody” for autographed items from the time they are signed by the athlete through all subsequent sales and transfers. In doing so, trust can be built into what has historically been an untrustworthy marketplace, assuring confidence and supporting the genuineness and value of items of sports memorabilia. The author presents both an overview of the sports memorabilia marketplace and RFID technology and follows up with a look at how RFID is being used today to authenticate and to track autographed items of all forms. The article concludes with a look ahead at the implications of the introduction of this new technology and a discussion of what lies ahead.
The Sports Memorabilia Market
A baseball is just a ball until it is signed by a star player. A jersey is just a big shirt until it is worn by an all-star. Then, such items are worth a lot of money, right? Oh, that it were that simple. The terms sports memorabilia and sports collectibles are all too often used interchangeably in the marketplace. According to the recent publication, A Comprehensive Guide to Collecting Sports Memorabilia, the two terms can be differentiated in the following manner: “Photos, cards, jerseys or related sports equipment that have been signed by an athlete are considered memorabilia when that signature has been certified by a reputable distributor. Replica and authentic sports products that are unsigned, or are signed but not authenticated, are considered collectibles” (SportsMemorabilia.com, 2008, n.p., emphasis in the original).
The sports memorabilia market can be segmented into two very distinct segments: trusted sources and other. Trusted sources include both sports memorabilia shows and sports marketing agencies (Fisher, 2000). In the former category, there are a growing number of such events, where athletes are available, generally on a paid basis, to sign a limited number of items, both brought in by fans and bought at the show. At these shows, items are signed, with witnesses present and able to authenticate the athlete’s signature on a certificate of authenticity (COA). This certification is what raises the status and value of an item from being a sports collectible to becoming an item of sports memorabilia (Branton, 2008). The second trusted source is the sports agencies that contract with athletes to be exclusive purveyors of their autographed merchandise. In the United States, the market leaders are companies such as the following:
Take Upper Deck for instance. This sports marketing agency has multi-million dollar contracts with current and former athletes from a whole host of sports, including basketball (NBA players Michael Jordan, LeBron James, Kobe Bryant, Dwight Howard, Kareem Abdul-Jabbar, and Magic Johnson), baseball (Albert Pujols, Ken Griffey Jr., Cal Ripken Jr., Sandy Koufax, Nolan Ryan, and Stan Musial), football (Peyton Manning, Tom Brady, Tony Romo, Troy Aikman, John Elway, and Joe Montana), and golf (Tiger Woods and Jack Nicklaus). Upper Deck is a market leader not just because of its status as the exclusive retailer for these star athletes of today and yesterday, but also for its 5-step certification process that stamps the item with a unique hologram and provides the owner with a certificate of authenticity and registration with the Upper Deck database. The company is even using with what it calls its PenCam™ technology, which the company had the misfortune to launch on September 11, 2001 (Henninger, 2002). The PenCam provides further authentication assurance by providing a video capture from–you guessed it–a pen equipped with a tiny video camera that captures the actual signature of the athlete on the item as it is being rendered, which is then recorded and accessible on the company’s database (The Upper Deck Company, 2008).
Items from trusted agencies do command premium prices, due to the fact that buyers and sellers alike have a very reliable chain of custody for their items of sports memorabilia. However, the vast majority of the sports memorabilia market is a murky, “other” place. In most cases, both offline and online, it is a very untrustworthy market, filled with intentionally counterfeited signed sports paraphernalia and fake items that are being bought and sold by mostly unknowing participants (SportsMemorabilia.com, 2008).
The entire sports memorabilia market in the U.S., and indeed around the world, is still reeling from the 2001 bust of a major fraud ring. In Operation Bullpen, the FBI arrested almost two dozen individuals, most of which served prison time for their involvement in the counterfeit sports memorabilia scheme. The enterprise, which operated across more than a dozen states, had expert forgers who could quickly produce entire lots of phony memorabilia. The 2001 raid yielded thousands of fraudulently signed baseballs, jerseys, helmets, photos, and other articles. The damage however, had already been done, and it continues to this day. In all, the FBI estimates that over $100 million in fake memorabilia was sold through the scheme, much of which is still on the market today, being traded by often-unsuspecting buyers and even sellers. The FBI found that not only could the forgers create knock-offs that could fool even the most knowledgeable sports memorabilia authenticator or collector, they uncovered that the criminals had turned the authentication process to their advantage. This is because the crooks were equally adept at falsifying the COAs and holograms put in place in the industry to assure the genuineness of the items (Nelson, 2006).
While 2001s Operation Bullpen was the largest fraud scheme uncovered in the sports memorabilia market to date, criminal arrests continue to plague the industry, with several cases reported in 2008 (Coen, 2008). The FBI estimates that such fraud makes for over a half a billion dollars in annual losses, impacting thousands of customers, and making it more difficult both for athletes to retain the value of their names and for legitimate firms to compete in a skeptical marketplace (Smith, n.d.; Johns, n.d.).
One of the major problem points for the whole memorabilia sales and trading process is the certificate of authenticity that accompanies an item. Ostensibly in place to provide a potential buyer with the assurance that the item he or she is considering purchasing is a genuine article, but today, the effect is almost the opposite. This is because of rampant fraud in the creation of these COAs. Today, there is no industry standard for certification process or for the paper COA itself. Thus, there are rampant problems with these documents. Some fraudulent memorabilia sellers create their own fake COAs to accompany their fake items (SportsMemorabilia.com, 2008; Smith, n.d.; Johns, n.d.). While there are several reputable third-party certification services, who will analyze an item and its history to determine its authenticity, there are also disreputable ones, known to certify, in the words of one law enforcement official, “almost anything” (Franks, 2006).
What is clearly needed today is a true chain of custody capability to authenticate items of sports memorabilia from the athlete’s signature through all future trades of the article. With the rampant fraud issues, which can only be exacerbated by both the high dollars attached to many athletes’ items and the accelerating technology that can be used to create both forged articles and proofs of authenticity, there is certainly a common interest for memorabilia collectors, athletes, sports marketing agencies, and the stores, shows and auctions (both online and offline) where the items are bought and sold to develop, for lack of a better term, a fool-proof solution. RFID presents the prospect for just such an incontrovertible chain of custody solution for this marketplace.
Radio Frequency Identification (RFID)
Conceptually, RFID is quite similar to the venerable bar code. Both are automatic identification technologies intended to provide rapid and reliable item identification and tracking capabilities. The primary difference between the two technologies is the way in which they read objects. With bar coding, the reading device scans a printed label with optical laser or imaging technology. However, with RFID, the reading device scans, or interrogates, a small electronic tag or label using radio frequency signals. The specific differences between bar code technology and RFID are summarized in Table 1. There are five primary advantages that RFID has over bar codes. These are as follows:
Each RFID tag can have a unique code that ultimately allows every tagged item to be individually accounted for.
RFID allows for information to be read by radio waves from a tag, without requiring line of sight scanning or human intervention.
RFID allows for virtually simultaneous and instantaneous reading of multiple tags.
RFID tags can hold far greater amounts of information, which can be updated.
RFID tags are far more durable. (Wyld, 2005)
Table 1
RFID and Bar Codes Compared
Bar Code Technology
RFID Technology
Bar Codes require line of sight to be read
RFID tags can be read or updated without line of sight
Bar Codes can only be read individually
Multiple RFID tags can be read simultaneously
Bar Codes cannot be read if they become dirty or damaged
RFID tags are able to cope with harsh and dirty environments
Bar Codes must be visible to be logged
RFID tags are ultra thin and can be printed on a label, and they can be read even when concealed within an item
Bar Codes can only identify the type of item
RFID tags can identify a specific item
Bar Code information cannot be updated
Electronic information can be over-written repeatedly on RFID tags
Bar Codes must be manually tracked for item identification, making human error an issue
RFID tags can be automatically tracked, eliminating human error
RFID is being introduced today across a variety of industries to better identify and control individual items, ranging from health care applications (Wyld, 2008 a, b) to the food service and gaming industries (Wyld, 2008c). Major retailers, such as Wal-Mart and Target in the United States and Metro and TESCO in Europe are making major investments in RFID technology, believing that this is the future of retail inventory control, supplanting the venerable bar code method of item identification (Wyld, 2007a, Wessel, 2008). Today, we are seeing exciting in-store applications in bookstores (Collins, 2006), pharmacies (O’Connor, 2008), electronics retailing (Swedberg, 2007a), and grocery stores (Swedberg, 2007b), bringing about new possibilities in customer service, business intelligence, and inventory management.
The RFID Solution for Sports Memorabilia
RFID has seen ongoing development in sports-related applications, being utilized in a variety of manners, from timing marathon runners and race cars to helping golfers find errant golf balls, and even off the field in the important areas of ticketing and event staff tracking (Wyld, 2006). Now, RFID is poised to become the latest weapon for retailers to deploy in this arms race against shoplifting, especially in light of the increasingly aggressive and sophisticated threat coming from the organized retail crime element.
The leading company today attempting to apply an RFID-based solution to authenticating sports memorabilia is the Irving, Texas-based Prova Group (http://www.provagroup.com/). Prova is currently marketing its patented Autograph Certification SystemTM for use at signing events and trade shows (Anonymous, 2008a). The concept, according to Daniel Werner, the firm’s Vice President of Marketing: “Prova decided early on to create a system that works at the moment of the signing that would put authentication in a database and lock that information onto an RFID tag” (quoted in Swedberg, 2007c, n.p.). As such, the tag is applied to the item prior to signing, and then, at the point of signing, the tag is read by and entered into the Prova database, recording who, when, and where the autograph took place. Once an item is registered in Prova’s Online Registry, the registered owner is able to print a certificate of authenticity on demand and to share the tagged item’s complete history, its chain of custody, with interested buyers or other collectors. Further, if a collector wishes to add additional signatures to an item (such as having an entire championship team autograph a football or basketball or adding the autograph of a current star, say Tony Romo, to a ball previously signed by a historic quarterback, such as Bart Starr or Joe Namath), the Prova RFID tag can record each separately and provide proof of authenticity for each autograph (Branton, 2008). The Prova system makes use of two form factors of high-frequency, 13.56 MHz passive tags for different sized collectibles, the smallest of which measures 1 inch by ¼ inch. Both of the tag forms are supplied by X-ident Technology (http://www.x-ident.com/), based in Düren, Germany. The system has been employed at special events where up to 4,000 items of memorabilia have been authenticated by Prova. And now, the firm is shifting from fixed reader stations to hand-held readers from Toronto-based Sirit (http://www.sirit.com/) to enable easier certification, as well as seeking ways to minimize the amount of data that has to be input to certify each individual autograph to speed the process (Swedberg, 2007c).
Analysis
Interjecting RFID into the sports memorabilia market certainly parallels other auto-ID technological applications, most notably pharmaceuticals (Faber, 2008) and government-issued forms of identification, including passports (O’Connor, 2007) and driver’s licenses (Anonymous, 2008b). With these application areas, there is a significant threat of counterfeit items. While there is undoubtedly a far greater threat of personal harm from the use of fake prescription drugs or the use of phony passports or ID cards than a forged signature of Alex Rodgriquez on a photo or baseball card, RFID has proven to be an effective solution in these areas. Furthermore, the high dollars involved means that the return on investment (ROI) potential is significant, as the ratio of the cost of the tag to the value of the item it is affixed to can be quite low indeed. Indeed, with an unauthenticated item basically being worthless, the need to shift to an auto-ID solution is quite clear. While the sports memorabilia industry is highly fragmented, with large agencies and thousands of small sellers, and perhaps millions of collectors, a coordinated strategy is highly unlikely. However, if the major sports marketing agencies choose independently or collectively to implement Prova or another provider’s RFID solution, this would go a long way toward making RFID-based authentication a reality in the sports memorabilia industry. As shown in Figure 1, this would help protect the interests of all legitimate players in the marketplace. In doing so, an industry, best known today for being susceptible to anyone with a box of baseballs and a SharpieTM pen, can restore trust and value to its marketplace.
Figure 1: The Value of RFID to the Sports Memorabilia Industry
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And in the end, the value of sports memorabilia can be protected. But also, the intrinsic value of the autograph experience can be as well. After all, as Olson (2006) commented: “For the true fan, the value of an autograph isn’t the signature itself. It’s the shared moment between a fan and his hero” (n.p.).
Author’s Note
David C. Wyld, the Robert Maurin Professor of Management and director of the e-Commerce and e-Government Initiative Department of Management, Southeastern Louisiana University in Hammond, Louisiana.
Correspondence concerning this article should be addressed to David C. Wyld, Department of Management, SLU – Box 10350, Hammond, LA 70402-0350. Email: dwyld@selu.edu
Submitted by: Timothy J. Henry, Robert C. Schneider, and William F. Stier Jr. – The State University of New York at Brockport
Abstract
In an effort to determine the importance of desirable qualities, attributes and characteristics necessary for the success of interscholastic athletic trainers a Likert-type scale survey was mailed to all head athletic trainers of NCAA Division III institutions in the United States. The survey consisted of 24 statements allowing for the following responses: essential, very important, important, not very important, and irrelevant. The qualities that were deemed the most desirable by head athletic trainers were trustworthiness (76.2%), honesty (73.5%), dependability (66.4%), and possessing high ethical standards (66.4%). The two characteristics that were found to be the least essential were being a risk-taker (2.1%) and being a visionary (6.4%).
Introduction
Certified athletic trainers (ATCs) are allied health care professionals who specialize in preventing, recognizing, managing, and rehabilitating injuries that result from physical activity. The ATC works as part of a complete health care team and functions under the direction of a licensed physician and in cooperation with other health care professionals, athletics administrators, coaches, and parents (NATA, 2006c). In order to become a certified athletic trainer, an individual must graduate from a Commission on Accreditation of Athletic Training Education (CAATE) approved Athletic Training program and successfully pass the Board of Certification Examination (NATA, 2006b).
The Board of Certification, Inc. (BOC) regularly conducts a role delineation study among a sample of certified athletic trainers. This study determines the current role, or standards, of the profession. This role delineation study may also be considered a job analysis and determines the minimal competencies to practice as an athletic trainer. It also serves to define the contemporary standards of practice for the athletic training profession (NATA, 2006a). The information gathered by this job analysis is used as a template for the NATA Educational Council to develop the Educational Competencies for Athletic Training. These competencies define the minimum skills and characteristics that entry-level athletic trainers should possess and define the educational content that students enrolled in an accredited athletic training program must master. The competencies are broken down into 12 content areas (Table 1) (NATA, 2006a).
“Athletic trainers are the critical link between the sport program and medical community” (Anderson and Hall, 2000, p. 6) and fulfilling this job requires the athletic trainer to fill many roles. In addition to the educational knowledge outlined by the educational competencies, ATCs must possess other qualities and attributes in order to succeed in the all-encompassing role of athletic trainer. Arnheim and Prentice (2000) describe some of these qualities as stamina and ability to adapt, empathy, sense of humor, communication, intellectual curiosity, ethical standards, and being active in professional organizations. Gaedek, Toolelian & Schaffer (1983) describe communication with other athletic trainers, physicians, physical therapists, and so forth as one of the primary attributes an ATC must possess.
Attaining a position in athletic training and, ultimately, success as an athletic trainer can be dependent upon several factors. Employers look for candidates who have both a formal and informal educational background (including certification from the BOC) as well as a demonstration of other skills and attributes that might have been obtained through experience as well as through formal educational courses (Gaedeke, Toolelian & Schaffer, 1983). When looking at employers’ hiring criteria for athletic trainers, the prevailing criterion predicting employment and salary is the educational status of the applicant (Kahanov and Andrews, 2001). This study by Kahanov and Andrews (2001) found that the four most important criteria for hiring were personal characteristics, educational experience, professional experience, and work-related attributes. Educational experience included a college minor, grade point average, membership in a fraternity, and college reputation. The personal characteristics included self-confidence, maturity, interpersonal skills, assertiveness, enthusiasm, technical skills, ability to articulate goals, oral communication skills, leadership skills, initiative, ambition, problem-solving skills, writing skills and personal appearance. Smith (2006, p.47) states that “certification and experience are important, but possibly even more critical are personality, character, and people skills”. Certified athletic trainers hold the key to a successful program, whether it is a professional team, a school, a physician’s office, a hospital, or a clinic. Thus, it is imperative to hire the right person for the job (Smith, 2006).
Although the literature contains many studies highlighting hiring criteria and desirable knowledge areas for ATCs, very few studies have investigated the personal characteristics and qualities of certified athletic trainers as viewed by employers in specific employment settings. The purpose of this study was to investigate the desired personal qualities, attributes, and characteristics of certified athletic trainers in the division III setting as viewed by head athletic trainers in these settings. To date, this is the only national study that surveyed all of the division III head ATCs asking them what personal qualities, attributes, and characteristics they believed to be important for the success of ATCs.
Methodology
Population:
The population surveyed included head athletic trainers of all NCAA division III colleges and universities. The mailing addresses of the colleges and universities were obtained from the NCAA headquarters located in Indianapolis, Indiana. Of the 410 surveys mailed out, 185 were returned for a return rate of 45.1%.
Survey Instrument:
The survey instrument utilized in the study was approved by the Institutional Review Board at the surveying institution. The instrument was developed based upon the professional literature and as well as communication with experts in the area of athletic training. Twenty-four specific skills and competencies were identified and included in the survey.
Procedures:
After approval of the survey instrument, all surveys were mailed to the NCAA division III head athletic trainers. A return envelope that was pre-stamped, and addressed to the principal investigator, was included in the mailings. Anonymity of the head athletic trainer, as well as the college and university surveyed, was ensured.
The head athletic trainers were asked to provide their opinions as to the level of importance of the personal qualities, attributes, and characteristics included on the survey that were related to the success of the athletic trainers in providing health care to student athletes. By responding to a 5-point Likert scale, essential, very important, important, not very important, irrelevant, the head athletic trainers provided their opinions as to the level of importance of specific skills and competencies found in successful athletic trainers.
Findings
The findings are displayed in Table 2 and revealed varied opinions regarding the importance of personal qualities, attributes, and characteristics that Division III head athletic trainers believed to be essential, very important, important, not very important, and irrelevant in order to be successful as an athletic trainer at the Division III level. Most of the items were identified as either essential or very important; however, some were not viewed as highly.
Six items were reported as the most important personal attributes for successful ATCs. These items had the highest percentage of responses as essential to the success of athletic trainers at the Division III level:
Trustworthiness (76.2%)
Honesty (73.5%)
High ethical standards (66.4%)
Dependable (66.4%)
Adaptable (62.7%)
Communicator (61.6%)
In addition to the attributes reported as essential, three items were reported as being highly desirable (either essential or very important) by 90% of the respondents:
Leadership (93.7%)
Decisiveness (91.8%)
Consistency (91.2%)
Head athletic trainers viewed the following as having the least impact (essential or very important) among all of the selected skills and competencies on success of the Division III ATCs:
Risk taker (19.9%)
High energy level (45.6%)
Visionary (46.9%)
Discussion
This study examined the desirable personal qualities and attributes necessary to be a successful athletic trainer at the Division III level. The most desirable characteristics reported by head athletic trainers in this study, honesty, trustworthiness, and high ethical standards, can be grouped together as ethical qualities. Each of these attributes is important to the ability of the ATC to provide high quality health care to the physically active. All members of the NATA are required to observe the NATA Code of Ethics, which provides an outline of ethical behavior that should be followed in the practice of athletic training. The Code is comprised of 5 principals and presents aspirational standards of behavior that all members should strive to achieve (NATA, 2006c). ATCs typically deal with many controversial and sensitive issues in which honesty, trustworthiness, and high ethical standards are of the utmost importance. Some of these sensitive situations may include athletes with diseases or conditions, such as HIV or hepatitis, athletes with sexually transmitted diseases, athletes with season-ending or career-ending injuries, and athletes that may be using, or are suspected of using, performance enhancing substances. In each of these scenarios, the ATC may find themselves exposed to extremely sensitive and confidential information. Confidential information that is obtained as part of the professional relationship that an ATC has with an athlete might be personal, private, and sensitive. The ATC should handle this sensitive information carefully to avoid ethical, as well as legal, breaches of confidentiality. Another issue related to the ethical standards of athletic trainers is the high profile of athletes and of the athletic industry in our society. The accessibility of the media and the public’s desire to know everything possible about their teams and athletes can be a significant threat to an athlete’s privacy and to the confidentiality of information to which the ATC is privy (Ray, 2005). The fact that the respondents in this study valued the ethical attributes establishes the importance of the Code of Ethics in the daily practice of the ATC.
Trustworthiness is not only important when dealing with the confidentiality issues, but it is extremely important in establishing a good rapport between the athlete and the athletic trainer. The athlete needs to respect the athletic trainer as a person before they can trust the athletic trainer in the rehabilitative setting. The ATC must gain the trust of the athlete before the athlete will follow the protocols and programs designed for them by their athletic trainer (Arnheim and Prentice, 2000).
Other attributes that were deemed highly desirable were adaptability and dependability. Arnheim and Prentice (2000, p. 16) report, “The athletic trainer must be able to adapt to new situations with ease.” This is due to the large number of athletes and teams that they are typically responsible for covering. Practice and game schedules are frequently canceled or modified, depending on factors such as weather, facility availability, team condition, travel schedules, and so forth. In many cases, ATCs are at the mercy of the coaches and administrators in determining these schedules and may not be consulted as to their opinions in those matters. Due to the unique skills which the ATC provides, they are difficult to replace and they must be present at all practices and contests in order to provide the high quality health care that the athletes deserve.
The ability to communicate is an attribute that was deemed essential by 61.6% of the respondents; however, we expected a higher percentage of the head athletic trainers to list this as essential. Athletic trainers are often described as a liaison between athletes, coaches, team physicians, and other allied health care professionals. This role requires the ATC to serve as an educator, psychologist, counselor, therapist, and/or administrator and is dependent upon a constant flow of oral and written communication (Arnheim and Prentice, 2000). Lockard (2005) stressed the importance of having positive relationships by stating that because athletic trainers deal with a variety of people, they need good social and communication skills.
Personal attributes that were deemed desirable by the respondents were decisiveness and leadership. Decisiveness is a characteristic that does not appear in any of the previous literature relating to desirable personal attributes or hiring characteristics for an ATC. During the course of any typical day for an ATC, many situations arise in which the athletic trainer must make important decisions. Referral decisions are an inherent part of the injury management domain of athletic training, especially those dealing with potentially catastrophic injuries. These decisions must be made spontaneously in many cases with the well-being of the athlete at stake.
The importance of leadership in our study is similar to the findings of Kahanov and Andrews (2001). They listed leadership as one of 16 characteristics that were viewed as important by employers when hiring ATCs across different job settings, although leadership was not rated as highly as other characteristics in their study. As mentioned previously, the ATC is typically the leader or coordinator of the sports medicine team (NATA, 2006e). Smith (2006) stated that certified athletic trainers hold the key to a successful program, whether it is a professional team, a school, a physician’s office, a hospital, or at a clinic.
The personal attribute that was reported to be the least important in the Division III setting was being a risk-taker. This finding is not surprising when considering the myriad of legal and ethical issues confronting ATCs today. Risk management is an important term to all ATCs today, and the athletic trainer is intimately involved in developing safe athletic programs in all types of settings. Lyznicki et al. (1999) found the implementation of risk management programs by athletic trainers to be important in that it minimized liability in secondary schools. Chen and Esposito (2004) recognized the importance of risk management and acknowledged the need for athletic trainers to formulate a risk management plan.
Another personal attribute that was not deemed essential to the success of athletic trainers at the division III level was high energy level. Only 16.2% of the respondents reported this to be essential, while 39.4 % rated this as very important. This finding is extremely surprising and is contrary to many commonly described views of the ATC. ATCs typically work extremely long hours and are asked to cover numerous sporting events every day. Arnheim and Prentice (2000, p. 16) state, “Athletic training is not the field for a person who likes an 8-to-5 job. Long, arduous hours of often strenuous work will sap the reserve strength of anyone not in the best of physical and emotional health. Athletic training requires abundant energy, vitality, and physical and emotional stability.” In recent years, the NCAA and other administrators have begun to recognize the long hours and busy days of ATCs and have implemented changes in the sports medicine coverage provided by ATCs. The NCAA recently implemented the guidelines for appropriate medical coverage for intercollegiate athletics (NATA, 2003), which generally increases the number of ATCs required to meet the health care needs of student athletes on NCAA college campuses. This document suggested to collegiate administrators that they need to hire more certified athletic trainers to cover the ever-increasing health care needs of their student athletes. This recently implemented guideline may have in fact alleviated some of the long hours and strenuous days that had become commonplace for the ATC. With the addition of more staff, head ATCs may now feel that having a high energy level is not as important as it was traditionally viewed.
Being a visionary is another characteristic that was not reported as desirable as some of the others. Athletic training is a relatively young profession and the physically active community is just beginning to recognize the role and importance of ATCs in providing health care to the physically active. The recent evolution of athletic training is due to the long-term vision of many early athletic trainers; however there are still many hurdles for ATCs to clear in order for athletic training to become fully integrated into the larger sports medicine field. Some of the important issues currently confronting NATA members are third party reimbursement, expanding employment settings, and refining the educational process. These are issues that many ATCs are concerned with and are highly intertwined with the long-term vision and strategic plan of the NATA. (NATA, 2006d). It is somewhat surprising to the authors that being a visionary is not deemed more desirable by head athletic trainers at the division III level.
Conclusion
The most important personal characteristics and attributes for ATCs at the division III level were related to ethical issues and included honesty, trustworthiness, and possessing high ethical standards. Other highly desirable characteristics were being adaptable, dependable, and a good communicator.
The least important personal attribute was being a risk-taker. Other attributes that, surprisingly, were not deemed as highly desirable were having a high energy level and being a visionary.
Table 1: Athletic Training Professional Competencies Areas
Risk Management and Injury Prevention
Pathology of Injuries and Illnesses
Orthopedic Clinical Examination and Diagnosis
Medical Conditions and Disabilities
Acute Care of Injuries and Illnesses
Therapeutic Modalities
Conditioning and Rehabilitative Exercise
Pharmacology
Psychosocial Intervention and Referral
Nutritional Aspects of Injuries and Illnesses
Health Care Administration
Professional Development and Responsibility
Table 2: Desirable Qualities, Attributes, and Characteristics of Successful Athletic Trainers
Qualities, Attributes, and Characteristics
Essential (%)
Very Important (%)
Important (%)
Not Very Important (%)
Irrelevant (%)
Honesty
73.5
20.5
1
1
4
Punctuality
45.9
42.1
8
2.1
1.6
Decisiveness
56.2
35.6
4.3
1.8
2.1
Trustworthiness
76.2
17.8
2.8
0
3.2
Consistency
47.5
43.7
5.7
1
2.1
Enthusiastic
12.4
52.4
29.9
2.1
3.2
High energy level
16.2
39.4
40.2
3.7
.5
Role model
28.6
43.2
23.9
2.7
1.6
Leadership
35.6
48.4
11.8
2.1
2.1
Persistence
20
50.4
25.9
2.1
1.6
Helpfulness
26.4
23.2
47.2
.5
2.7
Altruism
12.4
51.5
28.6
5.4
2.1
High ethical standards
66.4
28.9
1
.5
3.2
Visionary
6.4
40.8
44.3
7.5
1
Patience
35.1
45.6
15.6
1.6
2.1
Risk taker
2.1
17.8
44.7
30.8
4.6
Loyal
23.7
43.7
27.3
3.2
2.1
Dedicated
43.7
42.9
8.1
3.2
2.1
Adaptable
62.9
29.9
4
.5
2.7
Diplomatic
24.3
50.5
21
3.7
.5
Professional visual image
30.8
43.7
19.1
3.2
3.2
Communicator
61.8
31.3
3.7
.5
2.7
Empathetic
28.1
50.5
17.2
2.1
2.1
Dependable
66.4
29.9
.5
.5
2.7
Note: The values represent mean percentages of the Likert-type-scale responses.
References
Anderson, M. K., Hall, S. J, & Martin, M. (2000). Sports injury management and the athletic trainer. In Sports injury management. (2nd ed.) Baltimore, MD: Lippincott Williams & Wilkins.
Arnheim, D. D, & Prentice, W. E. (2000). The athletic trainer and the sports medicine team. In Principle of athletic training. (10th ed.) New York, NY: McGraw-Hill.
Chen, S., & Esposito, E. (2004). Practical and critical legal concerns for sport physicians and athletic trainers. Sport Journal, 7(2), Retrieved December 3, 2006, from http://www.thesportjournal.org/2004Journal/Vol7-No2/ChenEsposito.asp
Gaedeke, R., Toolelian D., & Schaffer, B. (1983). Employers want motivated communicators for entry-level marketing positions. Market News. 5, 1.
Kahanov, L., & Andrews, L. (2001). A survey of athletic training employers’ hiring criteria. Journal of Athletic Training, 36(4), 408-412.
Lockard, B. C. (2005). Athletic trainers: Providing healthcare for athletes of all kinds.
Occupational Outlook Quarterly, 49(1), 38-41.
Lyznicki, J. M., Riggs, J. A., & Champion, H. C. (1999). Certified athletic trainers in secondary schools: report of the council on scientific affairs, American Medical Association. Journal of Athletic Training, 34(3), 272-276.
National Athletic Trainers’ Association. (2006a) Athletic training educational competencies. (4th ed.). Dallas, TX: NATA.
National Athletic Trainers’ Association. (2006b). Athletic training education overview. Retrieved on November 20, 2006 from www.nata.org/consumer/docs/educationfactsheet05.pdf
National Athletic Trainers’ Association. (2006c). NATA Code of Ethics. Retrieved on January 30, 2007 from http://www.nata.org/codeofethics/code_of_ethics.pdf
National Athletic Trainers’ Association (2006d). Strategic Plan. Retrieved on January 27, 2006 from www.nata.org
National Athletic Trainers’ Association. (2006e). What is a certified athletic trainer?. Retrieved on November 20, 2006 from www.nata.org
National Athletic Trainers’ Association. (2003). Recommendations and guidelines for appropriate medical coverage of intercollegiate athletics. Retrieved on November 1, 2006 from www.nata.org/statements/support/amciarecsandguides.pdf
Ray, R. (2005). Ethics in sports medicine. In management strategies in athletic training. (3rd ed.) Champaign, IL: Human Kinetics.
Smith, L. (2006, November). Big job small staff. Training and Conditioning, pp. 47-51.
Author’s Note
Timothy J. Henry, Associate Professor and Athletic Training Program Coordinator, The State University of New York at Brockport; Robert C. Schneider, Associate Professor, Department of Physical Education and Sport, The State University of New York at Brockport; William F. Stier, Jr., Distinguished Service Professor and Graduate Director, Department of Physical Education and Sport, The State University of New York at Brockport.
Correspondence concerning this article should be addressed to Timothy J. Henry, Department of Physical Education and Sport, The State University of New York at Brockport, 350 New Campus Drive, Brockport, NY 14420. E-mail: thenry@brockport.edu; Fax: 585-395-2771; Work Phone: 585-395-5357.
Submitted by: Mary Allender – Pamplin School of Business – University of Portland
Abstract
As national interest in NASCAR grows, the field of sports economics is increasingly addressing various aspects of this sporting contest. The outcome of NASCAR races are of particular interest to fans, and, thus, models describing and predicting the outcome of NASCAR races are beginning to emerge. This paper builds a model predicting the outcome of NASCAR races using NASCAR data. Various forms of regression analysis were used as the methodology for this research. The outcome was hypothesized to depend on a set of variables and focused, in particular, on the importance of driver experience. The findings of this paper conclude that a driver’s years of experience do in fact play a significant role in predicting the outcome of NASCAR races.
Introduction
NASCAR is one of the fastest growing sports in the world. It generates 3 billion dollars a year in GDP and adds new fans to its loyal fan base each year. The academic study of NASCAR is in its infancy, and this paper seeks to add to that small but growing body of literature. The origins of NASCAR reach back to the days of prohibition, when the cars used by bootleggers needed speed while making delivery runs to avoid the authorities in pursuit. More horsepower was needed, and so began the quest to modify cars for more horsepower and reliability. Simultaneously, auto racing became a sport. The inaugural auto race at Daytona Beach took place on March 8, 1936 (Felden, 2005).
These early races, however, were not officially organized, and so races were haphazard and drivers tended to show up randomly. The original tracks often consisted of dirt or sand. Fans were few in numbers, thus driving stock cars remained a hobby, since it didn’t generate enough income to qualify as a job.
Over the next ten years, fan interest increased considerably, and stock car racing evolved from an occasional, hastily organized race on sand and dirt tracks to the stadiums and paved tracks we know today. In December 1947, Bill France Sr., both a driver and race promoter, developed the idea of NASCAR as organized stock car racing subject to specific rules. On February 15, 1948, NASCAR ran its first race at the Daytona Beach road course. The Daytona 500 remains the premier NASCAR race today. This paper proceeds in section II to discuss current research. Section III discusses data and methodology, while section IV discusses our empirical models and estimation methods. Section V discusses the findings of our analysis, and section VI offers concluding remarks.
Current Research
Scholarly research on NASCAR as a sport is relatively new and has taken many different directions. One avenue of research focuses on the reliability of NASCAR vehicles and explores the reasons behind part failure and the extent to which these critical part failures can be reduced. Majety, Dawande, and Rajgopal (1999) show that in general, the typical reliability allocation problem maximizes system reliability subject to a budget constraint. They note that cost is an increasing function of reliability and hence the tradeoff between dollars spent and system reliability. Although the media would have us believe that NASCAR owners are willing to spend virtually unlimited amounts of money to earn a spot in Victory Lane (New York Times, 2/13/06; CBS News, 10/6/05), NASCAR teams themselves acknowledge that in fact, a budget constraint does exist both in the form of willingness to spend money and the rules imposed on the construction of the vehicles themselves; although budgets in NASCAR racing are far more substantial than those common to commercially produced vehicles (Wachtel, 2006. Allender (2007), there continues with the reliability question, asking whether or not critical part failures in NASCAR vehicles are higher than what are expected and exploring some reasons as to why in fact they are.
Other lines of research focus on the type of tournament NASCAR represents and the most efficient type of reward structure for rank order tournaments (ROT), where finish position is all that matters to getting a prize. Becker and Harold (1992), Lynch and Zax (2000), and Maloney and McCormick (2000) use ROT theory to investigate the effect of different types of payment structures on the performance of contestants. Along similar lines, Lazear and Rosen (1981), Nabeluff and Stiglitz (1983), and O’Keefe, Viscusi, and Zeckhauser (1984) began to look seriously at a payoff structure that was preferable for the contest organizer. In fact, it was this line of research that began to take the field of sports economics into the realm of serious economic literature Fizel (2006).
Fans of NASCAR are ultimately interested in the outcome of each contest or race. The Nextel Cup Champion for the year, in essence, wins the majority of the points associated with the 38 races NASCAR holds each year at different tracks. Before the season and before each race, popular media focuses much attention on predicting the winner of each race. However, there is little in the sports economics literature that attempts to develop models that help predict the outcome of a NASCAR race. Pfitzner and Rishel (2005) develop a model predicting order of finish in NASCAR races based on variables such as car speed, driver characteristics, and the like. Allender (2008) develops a one season multivariate model showing that driver experience, along with other variables, is a statistically significant variable in determining the winner of NASCAR races. This paper seeks to add to that burgeoning body of literature by developing an empirical model that identifies the most important variables contributing to a driver’s success in a race. Thus, the model can be used as a tool in predicting the outcome of NASCAR races.
Data and Methodology
The pooled time series-cross sectional data set for the study spans the period 1990-2006. Each season consists of forty three cars and thirty eight races. The data were obtained from the NASCAR website. Our methodology utilizes regression analysis by estimating two slightly different models using weighted least squares. Our third model is a logistic regression model, which essentially converts the least squares model into a probabilistic regression model (Gujaarati, 1992).
Empirical Models and Estimation Methods
The basic model to be estimated is described in equation (1). FP represents finish position and is the dependent variable. SP represents starting position or pole position as determined during qualifying runs. We expect the sign on this explanatory variable to be positive. That is, the closer to the front the driver starts the race, the closer to the front he should be expected to finish. DY*SP represents the interaction between DY which is driver years of experience and starting position. We include this variable based on the theory that driver experience enhances the positive impact of starting position on finish position. Thus, the sign on this variable is expected to be positive. PC represents the percentage of laps under caution. Since caution laps freeze car position, we expect the sign on this explanatory variable to be positive since the more the caution flag comes out, the harder it is for cars coming from behind to make up laps. DY*TL represents the interaction term between driver years of experience and track length. We expect the sign on this variable to be negative. As the track length extends and works together with driver years of experience, we expect the driver, able to negotiate the various track lengths to move further toward the front.
Empirical Results
Initially, we test the model by estimating equation (1) using weighted least squares with driver years of experience used for weighting purposes. Table I reports these findings. Based on the t-statistics all model variables are statistically significant at the 1 percent level. Starting or pole position achieved during qualifying runs positively affects wining first place, which is what was expected. The interaction of variables SP and DY also show the right sign. The more experience in years a driver has, the higher his likelihood of winning. Therefore, as expected, the sign on this interaction variable is negative. The variable designated PC or percentage of laps under caution is showing a positive correlation to wining because while all drivers are affected by caution laps, our results show that more experienced drivers take advantage of this circumstance to take the lead.
Finally, the interaction of the variables DY and TL does not help a driver to advance to the top position. A possible explanation may be that on short tracks, more wrecks occur because more passing attempts are made on the curves, which are likely to eliminate, on a random basis the wrong driver, at the wrong time regardless of experience. More specifically, “bump drafting” as a strategy for passing on curves can be successful but depends not only on the experience of the driver attempting it, but also on the condition of the car being bumped which the driver attempting the maneuver has limited knowledge of.
Hence, we expect more randomness on short tracks.
Table 1
Dependant Variable: FP
Method: Least Squares
Date: 01/02/08
Sample (adjusted): 1 21698
Included observations: 21607 after adjustments
Weighting series: DY
Variable
Coefficient
Std. Error
t-Statistic
Prob,
C
12.04722
0.278278
43.29199
0.0000
SP
0.386505
0.012668-0.00263
30.51051
0.0000
DY*SP
-0.002630
0.000578
-4.551522
0.0000
PC
3.433934
1.284601
2.673152
0.0075
DY*TL
0.031246
0.005525
5.655003
0.0000
Weighted Statistics
R-squared
0.568955
Mean dependent var
20.73319
Adjusted R-squared
0.568875
S.D. dependent var
21.54092
S.E. of regression
14.14379
Akaike info criterion
8.136660
Sum squared resid
4321413.
Schwarz criterion
8.138507
Log likelihood
-87899.41
F-statistic
725.3306
Durbin-Watson stat
0.988600
Prob(F-statistic)
0.000000
Unweighted Statistics
R-squared
0.113075
Mean dependent var
21.36696
Adjusted R-squared
0.112911
S.D. dependent var
12.13023
S.E. of regression
11.42491
Sum squared resid
2819678.
Durbin-Watson stat
0.470986
Table II reports findings of weighted least squares for equation (1) with the added variable total life time winnings of a driver in dollars. This variable is designated W. The rationale for adding driver winnings in dollars as an explanatory variable is that the wining teams and drivers enjoy added resources which improve the quality of equipment, team members, and cars. All of these factors are expected to push a driver to a wining position in future races. Therefore, one expects a negative coefficient sign for this variable. Table II suggests that this hypothesis is correct and statistically statically significant. The remaining findings in Table II are qualitatively identical to those of Table I and in the interest of brevity we do not replicate that analysis. The R squared statistic for the two variations on equation (1) hovers under 60 percent which isn’t bad but suggests further research.
Table 2
Dependent Variable: FP
Method: Least Squares
Date: 01/02/08 Time: 16:07
Sample (adjusted): 1 21698
Included observations: 21607 after adjustments
Weighting series: DY
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
13.46957
0.273212
49.30084
0.0000
SP
0.339250
0.012376
27.41171
0.0000
DY*SP
-0.001703
0.000562
-3.031302
0.0024
PC
11.44432
1.267665
9.027874
0.0000
DY*TL
0.071924
0.005486
13.11113
0.0000
WI
-4.63E-05
1.29E-06
-35.92230
0.0000
Weighted Statistics
R-squared
0.593253
Mean dependent var
20.73319
Adjusted R-squared
0.593159
S.D. dependent var
21.54092
S.E. of regression
13.73968
Akaike info criterion
8.078731
Sum squared resid
4077810.
Schwarz criterion
8.080947
Log likelihood
-87272.57
F-statistic
872.9825
Durbin-Watson stat
0.950143
Prob(F-statistic)
0.000000
Unweighted Statistics
R-squared
0.157913
Mean dependent var
21.36696
Adjusted R-squared
0.157718
S.D. dependent var
12.13023
S.E. of regression
11.13263
Sum squared resid
2677131.
Durbin-Watson stat
0.409164
Table III reports estimation results of equation (2), that is, the logistic model. Based on the t-statistics, all variables in the model with the exception of the interaction variable TL*DY are statistically significant at the 1 per cent level. Variable SP shows that when its value is lower, the driver is starting further to the front, the higher the log of the odds of winning. This is as expected. Similarly, as before, the interaction of the variables PC and TL raises the log of the odds of winning. In contrast to our results of the weighted least squares estimation reported in Table I, the interaction of variables TL and DY turns out to be statistically insignificant in the logit model. This is unexpected and requires further investigation.
There is one possible explanation here, however. The tracks used in NASCAR range from three-quarter miles to two and a half miles in length with the vast majority being between 1 and 2 miles. In other words, there is so little variation in track length that the standard error on this explanatory variable is large. If you run track length as a stand alone explanatory variable, the t-statistic is low and makes track length an insignificant explanatory variable. In addition, the results show that more winnings in dollars for a driver, increases the log of the odds of winning races. However, the log likelihood number is a large negative number indicating that the model is a good overall fit.
Table 3
Dependent Variable: DUM1
Method: ML – Binary Logit (Quadratic hill climbing)
Date: 01/02/08 Time: 16:02
Sample (adjusted): 1 21698
Included observations: 21607 after adjustments
Convergence achieved after 9 iterations
Covariance matrix computed using second derivatives
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
-2.974485
0.116648
-25.49957
0.0000
SP
-0.089820
0.005735
-15.66153
0.0000
PC*TL
-4.204015
0.489954
-8.580419
0.0000
TL*DY
0.000310
0.003908
0.079394
0.9367
WI
1.51E-05
6.14E-07
24.54201
0.0000
Mean dependent var
0.024020
S.D. dependent var
0.153115
S.E. of regression
0.139077
Akaike info criterion
0.160929
Sum squared resid
417.8377
Schwarz criterion
0.162776
Log likelihood
-1733.598
Hannan-Quinn criter.
0.161531
Restr. log likelihood
-2447.999
Avg. log likelihood
-0.080233
LR statistic (4 df)
1428.803
McFadden R-squared
0.291831
Probability(LR stat)
0.000000
Obs with Dep=0
21088
Total obs
21607
Obs with Dep=1
519
Conclusion
This paper set out to develop an empirical model based on theoretical hypotheses to explain the finish position of drivers in NASCAR races. The model clearly identifies the most important variables that explain the finish position of each driver. This paper utilizes both a weighted least squares model and a logistic model to test our hypotheses regarding the variables most likely to influence the finish position of drivers in NASCAR races. These models produce promising results as demonstrated by the t-statistics and the R squared statistics.
This paper offers suggestions for further research. In order to improve R2, it may be advisable to explore the option of including additional explanatory variables. Another avenue worth exploring is how best to frame and utilize the variable associated with caution laps. Theoretically, the number of laps under caution is totally unpredictable prior to each race. Or is it? Are there some races that involve more crashes and hence caution laps than others? If that is not the case, then the randomness of caution laps would be picked up in the error term and contribute to a lower R2. On the other hand, again theoretically, the number of caution laps that occur during a race should have a significant effect on the outcome because caution laps allow for pit stops that give the crew time to make adjustments, add gasoline, and change tires, all of which should affect finish position. The broader question here is that the randomness factor plays a great role in NASCAR as a rank order tournament than it does in other rank order tournaments such as track and field.
References
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Author’s Note
Mary Allender, Pamplin School of Business, University of Portland, Oregon.
Correspondence concerning this article should be addressed to Mary Allender, University of Portland, 5000 N. Willamette Boulevard, Portland, Oregon 97203. Email: allender@up.edu
Submitted by: Chen-Yueh Chen – National Chung Cheng University (Taiwan) and Yi-Hsiu Lin – Aletheia University (Taiwan)
Abstract
Taiwan’s Chien-Ming Wang pitches for MLB’s Yankees, his performance drawing Taiwanese viewers to telecasts and making him renowned in Taiwan. The theory of planned behavior was employed to investigate why Taiwanese adolescents watch Wang’s televised games. The proposed model was analyzed with LISREL. Path analysis was performed for five hypotheses, namely (a) belief will positively affect attitude toward the act of viewing a game; (b) attitude toward the act will positively influence intention to watch; (c) perceived norm will positively influence intention to watch; (d) perceived behavioral control will positively affect intention to watch; and (e) perceived norm will positively influence attitude toward the act. The adolescents’ behavior was well explained by the theory, the data supporting all hypotheses.
Does Theory of Planned Behavior Explain Taiwan Teens’ Viewing of Televised NY Games With Pitcher Chien-Ming Wang?
Chien-Ming Wang is a Taiwanese baseball player who currently pitches for the New York Yankees of Major League Baseball (MLB). Wang is one of the league’s best, collecting 19 wins for the Yankees in the 2006 and 2007 seasons. Wang’s spectacular performance with the Yankees has meant increasing numbers of Taiwanese viewers for televised Yankees games—more specifically, for televised Wang games. Games have been televised in Taiwan since 1992, via a satellite sports channel. Their ratings are much higher now than in 1992, especially when Wang is pitching (Hu & Tsai, 2008). In short, it appears that Chien-Ming Wang has taken a place as one of Taiwan’s most famous sports celebrities.
Adoration of celebrities is particularly characteristic of adolescence (Lin & Lin, 2007). Reverence for sports celebrities is one of various forms of such adoration that adolescents often demonstrate (Greene & Adams-Price, 1990). In this study, we attempted to identify exactly what drives Taiwanese adolescents to watch the televised games in which Wang pitches. We used Ajzen’s theory of planned behavior (1985) to try to explain the adolescents’ behavior.
The theory of planned behavior (TPB) has been used in various domains (Chiou, Huang, & Chuang, 2005; Goby, 2006), for example in empirical studies from the field of marketing (Chiou, 2000; Taylor & Todd, 1995). TPB proposes three conceptually independent antecedents of intention: attitude toward the act, perceived norm, and perceived behavioral control (Ajzen, 1985). According to TPB, the attitude toward the act is the degree to which the individual evaluates the particular behavior favorably or unfavorably. The perceived norm describes the individual’s perception of social pressure to perform the act or not perform it. Perceived behavioral control, finally, reflects the extent of the resources for controlling the behavior which the individual perceives him- or herself to have.
TPB is an extension of the earlier theory of reasoned action proposed by Ajzen and Fishbein (1980). The addition of perceived behavioral control distinguishes the two. Perceived behavioral control is a critical factor, because people’s behaviors are strongly affected by how confident they are that they can perform those behaviors (Chiou et al., 2005). Generally speaking, the more favorable a person’s attitude toward an act, and the more strongly the person perceives the act as normative, and the more perceived control over the act, the stronger will be the intention to perform the act.
In addition, the cognitive-affective-cognitive framework proposes that “attitude structure starts with beliefs and is followed by affective response (e.g., attitude) and then cognitive responses (i.e., purchase intention)” (Chiou et al., 2005, p. 319). From this it follows that belief is an antecedent of attitude toward an act. Research has also shown that perceived norm is very likely to affect the formation of attitude (Oliver & Bearden, 1985; Terry & Hogg, 1996). That is, people’s attitudes may be influenced by their significant others.
Based on the literature, we proposed that attitude toward the act, perceived norm, and perceived behavioral control would positively influence Taiwanese adolescents’ intention to watch Wang pitch in a televised game. Furthermore, we proposed that belief and perceived norm would positively affect their attitude toward this act. Our hypotheses were the following:
Hypothesis 1: Belief will positively influence attitude toward the act.
Hypothesis 2: Attitude toward the act will positively influence intention to watch Wang’s game.
Hypothesis 3: Perceived norm will positively influence intention to watch Wang’s game.
Hypothesis 4: Perceived behavioral control will positively influence intention to watch Wang’s game.
Hypothesis 5: Perceived norm will positively influence attitude toward the act.
Method
Participants
Participants were students from two junior high schools, two senior high schools, and two universities (we limited participation at the latter to freshman students). They were sampled in April 2008. Participation was voluntary. The questionnaires were distributed by the participants’ teachers during a regular class meeting. Of 650 questionnaires distributed, 521 usable questionnaires were collected and used for analysis. The age of the participants ranged from 12 years to 20 years, with a mean of 16.11 years and a standard deviation of 2.18 years. There were 278 male and 243 female participants.
Measures
The measures of attitude toward the act, perceived norm, and perceived behavioral control were developed from Ajzen and Fishbein (1980), Azjen (1985, 1991), and Taylor and Todd (1995). The measures of intention to watch Wang’s game were modified from Chiou et al. (2005). Measures of belief were based on a focus group of 5 students; the participants were asked to reveal the most important attributes driving them to view televised games featuring Wang. The results showed that excitement, national pride, and the tension of the game were the most important such attributes. All measures employed a 7-point Likert-type scale.
Table 1
Items Measuring Latent Constructs Derived from Theory of Planned Behavior
Construct
Items
Perceived norm
Those who are important to me would consider my watching Wang’s game to be wise.
Those who are important to me would consider my watching Wang’s game to be useful.
Those who are important to me would consider my watching Wang’s game to be valuable.
Those who are important to me would think I definitely should watch Wang’s game.
Belief
To me, Wang’s game is exciting.
To me, Wang’s game is national pride.
To me, Wang’s game is a tension game.
Perceived behavioral control
I have full control regarding watching Wang’s game.
To me, to watch Wang’s game is what I can decide on my own.
It is up to me whether I will watch Wang’s game.
Attitude toward the act
My watching Wang’s game in the future would be favorable.
My watching Wang’s game in the future would be good.
My watching Wang’s game in the future would be wise.
My watching Wang’s game in the future would be useful.
Intention to watch Wang’s Game
I would watch Wang’s game in the future.
The probability that I would watch Wang’s game is high.
To me, (continuing to) watch Wang’s game is the best choice.
Data Analysis
The efficacy of the proposed model was analyzed using SPSS 14.0 and LISREL 8.51. Using LISREL with the maximum likelihood method, we tested the constructs and the measurement model for goodness of fit. A confirmatory factor analysis of the measurement model was conducted. The measurement model examined the relationships between 18 variables and 5 latent constructs (belief, perceived norm, attitude toward the act, perceived behavioral control, and intention to watch Wang’s game). Then, a path analysis was conducted to test whether identified antecedents of intention to watch a televised game featuring Wang reflected our hypotheses.
Results
Descriptive Statistics
The summated means for the constructs were 3.77 (perceived norm), 4.86 (belief), 4.97 (perceived behavioral control), 4.12 (attitude toward the act), and 3.81 (intention to watch Wang’s game). The standard deviations ranged from 1.73 to 1.98 (see Table 2).
Table 2
Mean, Standard Deviation, and Reliability of Constructs
Construct
M
SD
Cronbach’s α
Perceived norm
3.77
1.78
.93
Belief
4.86
1.73
.89
Perceived behavioral control
4.97
1.97
.91
Attitude toward the act
4.12
1.73
.91
Intention to watch Wang’s game
3.81
1.98
.91
Proposed Measurement Model
Overall model fit. The overall fit of the measurement model was found to be good. The root mean square error of approximation (RMSEA) value was .072, which is lower than the suggested threshold of .08 (Hu & Bentler, 1999). Additionally, the normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI), goodness of fit index (GFI), and incremental fit index (IFI) scores were .96, .97, .97, .91, and .97, respectively. All were greater than the suggested threshold of .90 (Hair, Black, Babin, Anderson, & Tatham, 2006), and each criterion of fit thus indicated that the proposed measurement model’s fit was acceptable.
Scale reliability. Cronbach’s alpha was used to evaluate the reliability of the constructs. The obtained values were .93 (perceived norm), .89 (belief), .91 (perceived behavioral control), .91 (attitude toward the act), and .91 (intention to watch Wang’s game) (see Table 2). Scale reliabilities for the constructs were acceptable according to the suggested threshold of .70 (Nunnally & Berstein, 1994, p. 265).
Construct validity. Construct validity refers to “the extent to which a set of measured items actually reflects the theoretical latent construct those items are designed to measure” (Hair et al., 2006, p. 776). Both convergent validity and discriminant validity should be achieved in order to fulfill construct validity (Hair et al., 2006). Convergent validity exists when “the items that are indicators of a specific construct . . . converge or share a high proportion variance in common” (p. 776), while discriminant validity indicates whether “a construct is truly distinct from other constructs” (p. 778). Standardized loading estimates above .5 indicate acceptable convergent validity, while evidence of discriminant validity is seen when the variance extracted for two factors is greater than the square of the correlation between the two factors (Hair et al., 2006).
In the present study, standardized loading estimates ranged from .80 to .97, indicating satisfactory convergent validity. In addition, the variance extracted for each construct ranged from .82 to .86, which was greater than the square of the correlation between two factors (which ranged from .30 to .79). Thus the study’s construct validity was also ensured.
Test of the Structural Model
Path analysis was used to test the fit of the proposed paths between constructs. The model fit of the path model was found satisfactory, with the RMSEA measuring lower (.074) than the suggested threshold of .08. The NFI, NNFI, CFI, GFI, and IFI were .99, .99, .99, .98, and .99, respectively, all greater than the suggested threshold of .90. All of the criteria for adequate fit indicated that the fit of the proposed structural model was satisfactory.
Hypothesis Testing
Figure 1. Path-analytic model: Influence on intention demonstrated by perceived norm, perceived behavioral control, and attitude toward act.
The results (see Figure 1) showed that perceived norm, attitude toward the act, and perceived behavioral control generated significant coefficients for intention to watch Wang’s game and also that perceived norm and belief generated significant coefficients for attitude toward the act. The path analysis produced the following measures: βat→iw = .34, t = 7.90, p < .001; γpn→iw = .44, t = 10.21, p < .001; γpbc→iw = .12, t = 4.29, p < .001; γpn→at = .43, t = 15.01, p < .001; and γbe→at = .52, t = 17.93, p < .001, where βat→iw refers to the β coefficient between attitude toward the act and intention to watch Wang’s game, γpn→iw stands for the γ coefficient between perceived norm and intention to watch Wang’s game, γpbc→iw means the γ coefficient between perceived behavioral control and intention to watch Wang’s game, γpn→at indicates the γ coefficient between perceived norm and attitude toward the act, and γbe→at refers to the γcoefficient between belief and attitude toward the act.
Additionally, the square multiple correlations were .68 and .80, respectively, for intention to watch Wang’s game and for attitude toward the act. The data analysis showed support for each of the study’s hypotheses. That is, belief positively affected attitude toward the act (H1); attitude toward the act positively influenced intention to watch Wang’s game (H2); perceived norm positively influenced intention to watch Wang’s game (H3); perceived behavioral control positively affected intention to watch Wang’s game (H4); and perceived norm positively influenced attitude toward the act (H5).
Discussion
Our study showed a goodness of fit for the proposed model that was satisfactory based on the various suggested criteria. All five hypotheses offered for the present study were supported by the data. A brief discussion of each path coefficient follows.
First, belief about the attributes of televised games featuring Wang’s pitching was a positive antecedent of attitude toward the act of watching. Beliefs about game attributes were described in items such as “Wang’s game is exciting,” “Wang’s game is national pride,” and “Wang’s game is a tension game.” As an antecedent of attitude toward act, a relatively strong belief that Wang’s performance was a source of national pride or that Wang’s games were exciting was an indicator of a relatively positive attitude toward watching a televised game featuring Wang.
Second, a participant’s attitude toward the act of viewing a televised game in which Wang will pitch positively influenced his or her intention to watch Wang’s game. This result illustrates that behavior is strongly affected by attitude (Blackwell, Miniard, & Engel, 2006). It follows that the more favorable the attitude toward the act of viewing Wang’s game, the stronger the intention to view it.
Third, perceived norm positively influenced the intention to watch Wang’s game. This relationship implies that peer pressure has an influence on whether adolescents watch a televised game. Such a finding is supported by the concept of the collectivistic society (Hofstede, 1983). People in a collectivistic society usually belong to a few in-groups (Hofstede, 1983). Securing a place in a group is important to adolescents (Chiou et al., 2005), but to be accepted by an in-group’s members (and to remain accepted by them), a would-be member must demonstrate his or her conformity to the in-group’s norms. Thus if an adolescent’s friends enthusiastically follow Wang’s game, it becomes necessary for the adolescent to follow Wang’s game as well, providing all in the group with common conversational themes, for instance. The idea applies similarly to the present finding of perceived norm’s positive influence on attitude toward the act.
Moreover, perceived behavioral control positively affected the adolescents’ intention to watch Wang’s game. This is an indication that perceived behavioral control is a positive antecedent of intention to watch Wang’s game, which is in line with Ajzen’s argument that the individual can be expected to carry out an intention when he or she has sufficient control over the behavior involved (1985). To sum up, the findings of the present study of Taiwanese adolescents’ behavior concerning the viewing of televised games featuring pitcher Chien-Ming Wang suggest that such behavior is well explained by Ajzen’s theory of planned behavior.
An interesting topic for future study would be adolescents’ adoration of sports celebrities. Specifically, researchers could investigate whether and how adoring a sports celebrity moderates the relationship of the variables included in the present study. They might ask, for example, whether the relationship between perceived norm and intention to watch Wang’s game is relatively strong among a group of adolescents who strongly admire or adore Wang, as compared to a group exhibiting less admiration.
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