ISSN: 1543-9518|Vol. 24

The Role of Driver Experience in Predicting the Outcome of NASCAR Races: An Empirical Analysis

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

Allender, Mary (2007). Are there a higher than expected number of early life critical part failures in NASCAR vehicles? A reliability Study. The Sport Journal. 25(1).

Allender, Mary (2008, May). Predicting the outcome of NASCAR races: The role of driver experience, Journal of Business and Economic Research.

Becker, B. E., & Harold, M. A. (1992). The incentive effects of tournament compensation schemes. Administrative Science Quarterly. 37, 336-350.

Depken, C. A., & Wilson, D. P. (2004). The efficiency of the NASCAR racing system: Initial empirical evidence. Journal of Sports Economics. 5(4), 371-386.

Gujarati, D. (2006). Essentials of econometrics. In Fizel, John (Ed.), Handbook of Sports Economics Research. London: M. E. Sharpe.

Lazear, E. P. & Rosen, S. (1981). Rank order tournaments as optimal labor contracts. Journal of Political Economy. 89(5), 841-864.

Majety, S. R., Dawande, M., & Rajgopal, J. (1999). Optimal reliability allocation with discrete cost-reliability data for component.” Operations Research, 47.6.

Maloney, M. T., & McCormick, R. E. (2000). The response of workers to wages in tournaments. Journal of Sports Economics, 1(2), 99-123.

Martin, M. (2005). NASCAR for dummies. (2nd ed.). Hoboken, NJ: Wiley & Sons.

Nalebuff, B. J., & Stiglitz, J. E. (1983, Spring). Prizes and incentives: Towards a general theory of compensation and competition. Bell Journal of Economics, 14, 21-34.

O’Keefe, M., Viscusi, K., & Zeckhauser, R. (1984). Economic contests: Comparative reward schemes. Journal of Labor Economics, 2(1): 27-56.

Pfitzner, B., & Rishel, T. (2005). Do reliable predictors exist for the outcomes of NASCAR races? The Sport Journal, 8(2).

Von Allmen, P. (2001). Is the reward system in NASCAR efficient? Journal of Sports Economics, 2(1), 62-79.

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

2013-11-25T20:01:01-06:00April 15th, 2009|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on The Role of Driver Experience in Predicting the Outcome of NASCAR Races: An Empirical Analysis
Read More

Does Theory of Planned Behavior Explain Taiwan Teens’ Viewing of Televised NY Games With Pitcher Chien-Ming Wang?

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
  1. Those who are important to me would consider my watching Wang’s game to be wise.
  2. Those who are important to me would consider my watching Wang’s game to be useful.
  3. Those who are important to me would consider my watching Wang’s game to be valuable.
  4. Those who are important to me would think I definitely should watch Wang’s game.
Belief
  1. To me, Wang’s game is exciting.
  2. To me, Wang’s game is national pride.
  3. To me, Wang’s game is a tension game.
Perceived behavioral control
  1. I have full control regarding watching Wang’s game.
  2. To me, to watch Wang’s game is what I can decide on my own.
  3. It is up to me whether I will watch Wang’s game.
Attitude toward the act
  1. My watching Wang’s game in the future would be favorable.
  2. My watching Wang’s game in the future would be good.
  3. My watching Wang’s game in the future would be wise.
  4. My watching Wang’s game in the future would be useful.
Intention to watch Wang’s Game
  1. I would watch Wang’s game in the future.
  2. The probability that I would watch Wang’s game is high.
  3. 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

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.

References

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp.11–39). New York: Springer-Verlag.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.

Blackwell, R. D., Miniard, P. W., & Engel, J. F. (2006). Consumer behavior (10th ed.). Mason, OH: Thomson Higher Education.

Chiou, J. (2000). Antecedents and moderators of behavioral intention: Differences between the United States and Taiwanese students. Genetic, Social, and General Psychology Monographs, 126(1), 105–124.

Chiou, J. S., Huang, C. Y., & Chuang, M. C. (2005). Antecedents of Taiwanese adolescents’ purchase intention towards the merchandise of a celebrity: The moderating effect of
celebrity adoration. Journal of Social Psychology, 145(3), 317–332.

Goby, V. P. (2006). Online purchase in an infocomm sophisticated society. Cyberpsychology and Behavior, 9(4), 423–431.

Greene, A. L., & Adams-Price, C. (1990). Adolescents’ secondary attachment to celebrity figures. Sex Roles, 23, 335–347.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Prentice Hall.

Hofstede, G. (1983). The cultural relativity of organizational practices and theories. Journal of International Business Studies, 14, 75–89.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.

Hu, W. L., & Tsai, M. H. (2008). The influence of sports-fan ethnocentrism on viewing motivations and behavior of sport broadcast. Physical Education Journal, 41(1), 51–68.

Lin, Y. C., & Lin, C. H. (2007). Impetus for worship: An exploratory study of adolescents’ idol adoration behaviors. Adolescence, 42, 576–588.

Nunnally, J. C., & Berstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.

Oliver, R. L., & Bearden, W. O. (1985). Crossover effects in the theory of reasoned action: A moderating influence attempt. Journal of Consumer Research, 12, 324–340.

Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–156.

Terry, D. J., & Hogg, M. A. (1996). Group norms and the attitude-behavior relationship: A role for group identification. Personality and Social Psychology Bulletin, 22(8), 776–793.

2016-10-20T09:59:29-05:00January 8th, 2009|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off on Does Theory of Planned Behavior Explain Taiwan Teens’ Viewing of Televised NY Games With Pitcher Chien-Ming Wang?
Read More

A New Scale Measuring Coaches’ Unethical Behaviors for Comparison by Gender, Age, and Education Level of Coach

Submitted by: Semiyha Dolaşir Tuncel – Ankara University

Abstract

An effort to develop a scale measuring coaches’ unethical behaviors included two phases. In the first, factor and reliability analyses were made of potential survey items meant to gather data from athletes describing coaches’ behavior. In the second, select items were incorporated in a survey randomly administered to 221 male and female taekwondo competitors at a national competition in 2006, for comparison of behaviors by coach gender, age, and education. Behavior was not found to differ significantly by gender (n = 219, t = 1.71, p > .05), age (n = 216, t = 1.13, p > .05), or education (n = 217, t = 1.60, p > .05).

A New Scale Measuring Coaches’ Unethical Behaviors for Comparison by Gender, Age, and Education Level of Coach

In coaching, a code of ethics is a tool providing a minimum standard of conduct and behavior expected of the coach as he or she develops into a professional. Many other professions, including medicine and law, also expect members to adhere to a behavior code requiring them to do their best and maintain professional standards (Ring, 1992). Codes established for coaches provide common values and guidelines for performing one’s job.

It has been suggested that there is a sensitive relationship between physical education and moral education. Stoll (1995), who is with the University of Idaho Center for Ethical Theory and Honor in Competitive Sports, emphasized that “physical education and athletic programs could be harmonious in promoting the development of sportsmanlike behaviors, ethical decision-making skills, and a total curriculum for moral character development.” Many studies by philosophers of sport concern the relationship of moral education and competition concepts; many conclude that a completed sports education involving both competition and development of an understanding of fair play effects a moral education (i.e., an education in moral values such as honesty, equality, justice, and respect) (Bergmann, 2000; Carr, 1998; Priest, Krause, & Beach, 1999; Singleton, 2003; Spencer, 1993). Sabock (1985) argued that sports provide students an important opportunity to develop ethical behaviors including honesty and fairness. Bergmann (2000) noted a logical relationship between physical education and moral education, one based on students’ understanding of the concept of success and their acceptance of the importance of competitions. Bergmann added that, through competition, students have opportunities to compare their skills and talents to those of others, which motivates them to gain practical knowledge meeting certain standards.

As role models for athletes, coaches can help them develop fair and ethical behavior by demonstrating how these can be applied in sports. Coaches have the capacity to teach and reinforce ethical behavior by athletes and indeed are central to value development in young people, since they are role models of institutional norms (Wandzilak, 1985).

Today, however, unethical behavior exhibited in the course of coaching is decreasing respect for coaches and for sports. Too many coaches approach their duties without adequate regard for values such as honesty, objectivity, and justice. This is so despite the fact that many sports organizations and communities have published codes of ethics that coaches are expected to uphold (American National Youth Sports Coaches Association, n.d.; American Psychological Association, 1992; Australian Sports Commission, n.d.; British Institute of Sports Coaches, n.d.; Canadian Professional Coaches Association, 2003; International Coaches Federation, 2003; Sports Medicine Australia, n.d.; Sports Coach, n.d.). Figure 1 presents a summary of the standards set out by these codes of conduct, classifying them as either a responsibility of coaches or a form of respect coaches are expected to demonstrate.

Responsibility Respect
1. A coach should provide a healthy environment for competition and practice.2. A coach should always work toward personal development, in order to continuously improve his or her job performance.

3. A coach should provide the media and members of the public with correct information.

4. A coach should direct injured athletes to medical treatment and act in accord with medical professionals’ instructions and suggestions.

5. A coach should help athletes with their personal and family problems.

6. A coach’s support should extend to athletes in need, whether or not they are his or her own athletes.

7. A coach should work cooperatively with any expert who might contribute to the development of athletes.

8. A coach should inform athletes of how they should behave during media interviews.

9. A coach should not use training techniques that are harmful to athletes.

10. A coach should select equipment carefully to ensure athletes’ safety.

11. A coach should have the injured athlete’s well-being in mind when deciding whether to permit a return to competition and should never permit return ahead of complete recovery.

12. A coach should assign athletes appropriate responsibilities in order to contribute to their development.

13. A coach should take a protective stance toward athletes when it comes to harmful drugs, by informing athletes about drugs’ dangers.

14. A coach of nonprofessional athletes should schedule practice and competitions that do not interfere with athletes’ need to develop academically.

15. A coach should develop effective ways of communicating to athletes and their families their rights and responsibilities as part of the team.

16. A coach should emphasize education’s importance to athletes, as well as sports’ importance.

17. A coach should instill in athletes the idea that winning results from good team work.

18. A coach should always ensure that athletes receive an explanation of the objectives of training.

19. A coach who disciplines an athlete through punishment should not, in so doing, harm the athlete’s personality.

20. A coach should always explain for athletes the objectives of any rule that will be applied.

 1. A coach should have respect for each athlete’s being.2. A coach should avoid behavior that is likely to diminish the respect afforded him or her by the society.

3. A coach should not exaggerate his or her capabilities.

4. A coach should encourage fair play and sportsmanlike behavior.

5. A coach should keep confidential all personal information on athletes (e.g., personal problems, family problems) and all information about the coach’s job (e.g., budget, recruitment policy), unless disclosure is required by law.

6. A coach should emphasize honesty in competition.

7. A coach should respect the rules of competition.

8. A coach should respect written and unwritten rules of fair play.

9. A coach should respect decisions of referees during competitions.

10. A coach should not encourage athletes or spectators to disrespect referees.

11. A coach should always have his or her behavior under control.

12. A coach should not use negative words to criticize other coaches or organizations.

13. A coach should take responsibility in areas in which he or she feels confident.

14. A coach should not criticize athletes publicly or act to hurt them.

Figure 1. Summary of coaching behaviors mandated by various organizational codes of ethics.

When such standards are ignored, unethical coaching behaviors typically fall into four main categories, according to the United States Olympic Committee (DeSensi & Rosenberg, 1996). They are (a) offending athletes verbally or physically, (b) treating athletes inhumanely, (c) encouraging athletes’ use of performance-enhancing drugs; and (d) ignoring the athletic program’s educational goals. In its various forms, unethical behavior in coaching is becoming an important topic in the physical education literature. The present study’s purpose was to develop a valid and reliable scale measuring the extent of unethical behavior by coaches and then to test whether their unethical behavior was associated with gender, age, or educational level.

Method

Sampling and Research Design

The study collected data in 2006 from 221 competitors in a national taekwondo championship, 86 of whom were female (38.9%) and 135 of whom were male (61.1%). The majority of the sample (76.9%) were ages 17 to 23 years. The mean length of their experience in taekwondo was 7 ± 3 years. The average age at which they began high-performance training (attending training camps and national and international competitions regularly) was 8 ± 2 years.

Instruments and Data Collection

The instrument was developed in three phases. First, from a review of the codes of ethics of the American National Youth Sports Coaches Association (n.d.), American Psychological Association (1992), British Institute of Sports Coaches (n.d.), Canadian Professional Coaches Association (n.d.), International Coach Federation (n.d.), Sports Medicine Australia (n.d.), Sports Coach (n.d.), and several Olympic committees, a pool of 48 survey items was created and subsequently analyzed.

Second, with the 48 items providing a basis, an instrument was developed that used a 5-point Likert-type response scale ranging from 1 (strongly disagree) to 5 (strongly agree) to assess perceived ethical or unethical nature of coaching behaviors (see Table 1). This instrument was administered to a group of 18 taekwondo coaches, taekwondo players, and faculty members or instructors knowledgeable of the sport. They read each item on the instrument and circled a response. The 18 participants unanimously assigned a score of 5 to 35 of the items, so these 35 were accepted by the researcher as describing unethical behaviors (Balci, 1993). The scale was dubbed the Coaches’ Unethical Behaviors Scale, or CUBS.

Table 1

Score Levels Reflected in 5-Point Likert-Type Scale

Choice Score Level
1 Strongly disagree 1.00–1.79
2 Disagree 1.80–2.59
3 Undecided 2.60–3.39
4 Agree 3.40–4.19
5 Strongly agree 4.20–5.00

In the third phase, the final CUBS instrument of 35 items (with 5-point Likert-type response categories) was administered to the 221 taekwondo contestants. Each item posed a scenario involving coaching behavior; respondents circled the numeral indicating how strongly they agreed that they had experienced their coaches demonstrating the unethical behavior.

Statistical Analysis

The construct validity of CUBS was evaluated using exploratory factor analysis (EFA). EFA seeks to identify a factor or factors based on relationships among variables (Kline, 1994; Stevens, 1996; Tabachnick & Fidell, 2001). The reliability of CUBS was assessed using the Cronbach’s alpha coefficient and Spearman-Brown (split-half) correlation. In order to test whether coaches’ unethical behaviors change with gender, age, and educational level, a t test and one-way ANOVA analysis were applied.

Findings

Factor Structure of CUBS: Construct Validity

Results of exploratory factor analysis assessing CUBS’ validity showed 11 of the 35 items to have a factor loading below .45. These 11 were extracted, and the analysis was repeated with the remaining 24 items. Of these, 14 could be classified as pertaining to coaches’ responsibility for athletes, for rules, and for the integrity of the coaching profession; the 14 became Factor 1. The remaining 10 could be classified as forms of respect coaches are charged with upholding (for example, respect for individuals, personalities, gender, and health). These became Factor 2.

For Factor 1, factor loading ranged from .562 to .847, while for Factor 2 it ranged from .561 to .782. Factor 1 accounted for 50.34% of variance, and Factor 2 accounted for 11.31%, so together the factors accounted for 61.65% of total variance (see Table 2).

Item Factor 1 Factor 2 Communalities Variance
1 .562 .466 .533
2 .589 .424 .527
3 .761 .359 .708
4 .674 .426 .635
5 .719 .352 .641
6 .641 .436 .601
7 .758 .155 .599
8 .747 .192 .594
9 .794 .328 .738
10 .833 0.61 .698
11 .811 .228 .710
12 .720 .285 .600
13 .847 .262 .786
14 .834 .281 .774
15 .777 0.46 .606
01 .211 .675 .500
02 .301 .721 .611
03 .377 .561 .456
04 .236 .667 .501
05 .131 .709 .519
06 .191 .737 .580
07 .308 .782 .706
08 0.94 .753 .576
09 .180 .752 .597

Reliability

The reliability of CUBS was assessed using Cronbach’s alpha and the Spearman-Brown correlation. The Cronbach’s alpha coefficients indicate internal consistency; for the two CUBS subscales administered to the 221 athletes, Cronbach’s alpha was .78 for Factor 1 and .77 for Factor 2. The total internal consistency for the scale was .76. The Spearman-Brown correlation yielded .98 for Factor 1 and .93 for Factor 2. Total correlation for CUBS was thus .92.

Corrected item total correlations, which ranged from .63 to .87, are shown in Table 3, along with t-test scores for the items in CUBS. Statistical significance at a level of p < .01 was attained for each item’s mean score.

Table 3

Corrected Item Total Correlations and t Scores for Items in CUBS

Item Factor 1 Factor 2 t p
1 .67 -7,122 .000
2 .70 -8,587 .000
3 .81 -9,341 .000
4 .77 -10,376 .000
5 .79 -10,645 .000
6 .76 -10,468 .000
7 .74 -9,826 .000
8 .75 -11,786 .000
9 .86 -11,590 .000
10 .78 -9,253 .000
11 .82 -12,238 .000
12 .76 -11,763 .000
13 .87 -14,444 .000
14 .86 -9,477 .000
15 .69 -11,574 .000
01 .67 -11,814 .000
02 .74 -9,108 .000
03 .63 -12,701 .000
04 .66 -10,988 .000
05 .74 -10,084 .000
06 .68 -10,174 .000
07 .74 -12,483 .000
08 .81 -11,849 .000
09 .70 -10,783 .000

Unethical Behaviors of Coaches

Using the data from the surveyed taekwondo competitors, coaches’ unethical behaviors were measured with descriptive statistics (see Table 4). As Table 4 illustrates, the athletes reported they had observed in the behavior of their coaches the 24 unethical behaviors reflected in CUBS, although the values measured for these behaviors were low. Observed unethical behavior did not, according to t-test results, appear significantly dependent on gender (n = 219, t = 1.71, p > .05), age (n = 216, t = 1.13, p > .05), or education level (n = 217, t = 1.60 p > .05).

Table 4

Mean, Standard Deviation, and Percentages for Coaches’ Unethical Behaviors as Indicated by CUBS Respondents

Unethical Behaviors M SD %
Responsibility
1. The coach does not deal honestly with athletes. 1.56 1.01 5.50
2. The coach does not inform athletes about harmful effects of drugs (drug abuse). 1.75 1.14 12.70
3. The coach does not build respectful, effective communication with athletes. 1.60 0.95 4.10
4. The coach encourages athletes’ weight loss via means that may harm their health. 1.75 1.02 7.30
5. The coach does not provide athletes necessary information about training. 1.61 0.98 7.70
6. The coach does not continuously improve his or her professional knowledge and skills. 1.72 1.16 10.90
7. The coach does not care about honesty in competition. 1.80 1.17 10.40
8. The coach does not know the legal regulations relevant to his or her sport. 1.53 1.00 5.00
9. The coach does not have sufficient knowledge of training science. 1.73 1.16 13.6
10. The coach abuses his or her authority as a coach. 1.61 0.99 6.80
11. The coach is not honest about the finances of competition. 1.62 1.04 5.90
12. The coach does not prepare effective training programs reflecting athletes’ ability levels. 1.84 1.11 7.20
13. The coach does not evaluate athletes’ performances as they reflect established goals. 1.66 1.00 5.90
14. The coach does not provide athletes with feedback about their performances. 1.68 0.99 7.20
Respect
1. The coach does not treat athletes respectfully. 1.39 0.95 5.90
2. The coach discriminates among athletes based on gender, religion, or language. 1.44 0.82 3.20
3. The coach curses or uses street language. 1.41 0.77 9.00
4. The coach does not respect the being of the athletes. 1.42 0.76 3.60
5. The coach is not careful to avoid harming athletes’ personalities when using punishment to discipline them. 1.56 0.89 5.50
6. The coach causes athletes physical harm in the course of using punishment to discipline them. 1.61 0.95 7.70
7. The coach discriminates among athletes based on reasons other than individual merit. 1.97 1.22 15.00
8. The coach degrades athletes with insults. 1.52 0.87 6.40
9. The coach becomes publicly angry and displays violence after a defeat in competition. 1.62 1.02 8.60
10. The coach does not respect rules and referees. 1.67 1.04 6.80

Discussion and Results

The present study’s purpose was to develop a valid and reliable scale measuring the extent of unethical behavior by coaches and then to test whether their unethical behavior was associated with gender, age, or educational level. CUBS is such a scale, according to the results of factor and reliability analysis (Kline, 1994; Stevens, 1996; Tabachick & Fidell, 2001).

Data obtained with CUBS were subjected to descriptive statistical analysis that suggested the three most frequent unethical behaviors in coaching are discrimination among athletes based on reasons other than individual merit; lack of technical knowledge; and failure to offer athletes facts about harmful drug use. Coaches’ unethical behaviors did not change to a significant degree with changes in gender, age, or education level, according to ANOVA and t-test results.

Addressing ethical issues is becoming a standard part of a coach’s duties. Increasingly, sports coaches must be able to teach and model fair play, respect for officials, paramount concern for athletes’ well-being (rather than the win-loss record), and the wise and legitimate use of power. At the same time, they must steer athletes away from harmful drug use, cheating, bullying, harassment, and eating disorders. The coach’s position on these issues, reflected in his or her coaching behaviors, has enormous impact on athletes, shaping their enjoyment of sports, their attitudes toward their peers in a sport, their self-esteem, and their continued involvement in sports.

The sports ethicist’s basic goal is to see individuals in sports accept a pertinent ethical code (Wuest & Bucher, 1987) and embody that code in their behavior patterns. The aim for the profession of coaching is each coach’s acceptance of an ethical code for his or her sport, exhibited in daily behavior. A scale like CUBS can not only indicate the level of unethical behaviors coaches engage in, it can point the way to the most urgently needed additions to coach education and development programs.

Knowledge and skills are vital to a profession, but appropriate attitudes and behaviors—professional ethics—are just as important. Professional ethics involve written codes containing rules tailored to specific professions and founded in general moral values like honesty, equality, justice, and respect (Fain, 1992; Pritchard, 1998). Unlike in the past, a workforce today is likely to include people of various races, ages, religions, educational levels, and socioeconomic statuses. They are likely to possess divergent values (Lankard, 1991; Frederick, Post, & Davis, 1988). Inculcating a set of professional ethics ensures that, although they are very different people, members of a profession together espouse common standards and rules designed to protect both themselves and the people they serve. The changing nature of the business world has increased the need for professional ethics, the most important characteristic of which is the need for systems, structures, and management that can secure compliance.

A common understanding of sports is that they consist of various activities people pursue that lead to competition (Penney & Chandler, 2000). In fact, sports is a multidimensional phenomenon. It involves social structures (an indispensable part of human life), and it is based on long-established ethical and value systems (Whitehead, 1998). A number of sports organizations want to see the essential ethical nature of sports brought home to spectators and the society by developing athletes’ and coaches’ ethics (Wuest & Bucher, 1987).

Concern for ethics (or the lack of concern) will have an important role in how sports continues to develop; much of the related work will fall to coaches, who are expected to do their jobs honestly, objectively, openly, and with respect and a sense of justice, tying their work to universal values and principles (Wuest & Bucher, 1999). Coaches who may be held responsible for demonstrating ethical behaviors need, first of all, to understand their sports’ particular ethical codes.

The present study was the very first research conducted in Turkey into unethical behaviors exhibited in coaching. Moreover, to date the literature worldwide has offered few studies on coaches’ unethical behaviors. For this reason, further research employing various designs, with various samples, is likely to contribute to understanding of the topic.

References

American National Youth Sports Coaches Association. (n.d.). Coaches’ code of ethics. Retrieved March 22, 2004, from http://www.nays.org

American Psychological Association. (1992). Ethical principles of psychologists and code of conduct.

American Psychologist, 47(12), 1597–1611.

Australian Sports Commission (n.d.). Ethics in sports: Code of behavior. Retrieved August 31, 2007, from http://www.ausport.gov.au/ethics

Balci, A. (1993). Research in social science: Method, technique and principles. Ankara: Öncü.
Bergmann, D. S. (2000). The logical connection between moral education and physical education. Journal of Curriculum Studies, 32(4), 561–573.

British Institute of Sports Coaches. (n.d.). Code of ethics and conduct for sport coaches. Retrieved April 26, 2002, from http://www.brianmac.co.uk/ethics.htm

Canadian Professional Coaches Association (n.d.). Coaches of Canada coaching code of ethics: Principles and ethical standards. Retrieved December 19, 2008, from http://coach.ca/eng/certification/documents/REP_CodeofEthics_01042006.pdf

Carr, D. (1998). What moral educational significance has physical education? A question in need of disambiguation. In M. J. McNamee & S. J. Parry (Eds.), Ethics and sport (pp. 119–133). London: E & FN Spon.

DeSensi, J. T., & Rosenberg, D. (1996). Ethics in sports management. Morgantown, WV: Fıtness Information Technology.

Fain, G. S. (1992). Ethics in health, physical education, recreation and dance. (Report No. ED342775 1992-04-00). Washington, DC: ERIC Digest. (ERIC Document Reproduction Service No. ED342775)

International Coach Federation (n.d.). The ICF code of ethics. Retrieved December 18, 2008, from http://www.coachfederation.org/ICF/For+Current+Members/Ethical+Guidelines/

Kline, P. (1994). An easy guide to factor analysis. New York: Routledge.

Lankard, B. A. (1991). Resolving ethical dilemmas in the workplace: A new focus for career development. (Report No. ED334468 1991-00-00). Washington, DC: ERIC Digest. (ERIC Document Reproduction Service No. ED334468)

Penney, D., & Chandler, T. (2000). Physical education: What future(s)? Sport, Education and Society, 5(1), 71–87.

Priest, R. F., Krause, J. V., & Beach, J. (1999). Four-year changes in college athletes: Ethical value choices in sports situations. Research Quarterly for Exercise and Sport, 70, 170–178.

Ring, J. J. (1992). An alliance to excellence: To preserve medical professionalism. Vital Speeches of the Day, 58(12), 367–368.

Sabock, R. (1985). Coach (3rd ed). Champaign, IL: Human Kinetics.

Singleton, E. C. (2003). Rules? relationships?: A feminist analysis of competition and fair play in physical education. Quest, 55, 495–209.

Spencer, A. F. (1996). Ethics in physical education and sport education. Journal of Physical Education, Recreation and Dance, 67(7), 37­–39.

Sports Medicine Australia. (n.d.). Sports Medicine Australia sports first-aider/sports trainer code of ethics. Retrieved December 19, 2008, from http://www.sma.org.au/sportstrainers/ethics.asp

Stevens, J. P. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

Stoll, S. K. (1995). Should we teach morality?: The issue of moral education. In A. E. Jewett, L. L.

Bain, & C. D. Ennis (Eds.), The curriculum process in physical education (2nd ed.), 334.

Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.

Sports Coach (n.d.). Code of ethics and conduct for sports coaches. Retrieved December 19, 2008, from http://www.brianmac.co.uk/ethics.htm

Wandzilak, T. (1985). Values development through physical education and athletics. Quest, 37, 176–185.

Whitehead, M. E. (1998). Sport ethics and education. Sport, Education and Society, 3(2), 239–241.

Frederick, W. C., Post, J. E., & Davis, K. (1988). Business and society: Corporate strategy, public policy, ethics (6th ed.). Columbus, OH: McGraw-Hill.

Wuest, D. A., & Bucher, C. A. (1986). Foundations of physical education and sport (10th ed.). St. Louis, MO: Times Mirror/Mosby.

Wuest, D. A., & Bucher, C. A. (Eds.). (1999). Foundations of physical education and sport (13th ed.). New York: WCB/McGraw-Hill.

2017-08-07T11:39:17-05:00January 8th, 2009|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on A New Scale Measuring Coaches’ Unethical Behaviors for Comparison by Gender, Age, and Education Level of Coach
Read More

Sports Tourism in Cyprus: A Study of International Visitors

Submitted by: Nicos L. Kartakoullis, Stavros Pouloukas and Christina Loizou – University of Nicosia, George Karlis – University of Ottawa and Thanos Kriemadis – University of the Peloponnese

Abstract

A decline in the number of tourists visiting Cyprus from 2000 to 2007 prompted the Cyprus Tourism Organization to examine sports tourism as a means of appealing to international visitors. Face-to-face interviews were conducted at airports in the cities of Larnaca and Pafos with 802 international tourists departing Cyprus. The respondents were surveyed about their experiences with three types of sports tourism in Cyprus: competitive (elite- and amateur-level athletic training or other preparation as well as competition), recreation (competition without trophy rewards), and leisure (sports-related play or pastimes). Statistical analysis showed most respondents had engaged in swimming, water sports, or other leisure-type sports tourism, with minimal numbers participating in the other two types.

Sports Tourism in Cyprus: A Study of International Visitors

In industrial nations, sports tourism contributes 1% to 2% of gross national product, while the contribution of tourism in general is 4% to 6% (Hudson, 2003). In the United States, the Travel Industry Association (TIA) reports that the crisis in tourism following the September 11 terrorist attacks in New York and elsewhere did not extend to sports tourism; the number of sports tourists remained steady (Neirotti, 2005). Although sports tourism has been an emerging trend in the tourism industry only since the mid-1990s (Gibson, 1998; Hinch, Jackson, Hudson, & Walker, 2005), it seems to be one form of tourism not marked by decline during difficult times (Karlis, 2006).

The nation of Cyprus has traditionally relied on the sun and sea in marketing its tourism industry. But a recent steady decrease in tourism in Cyprus (during 2000–2007, visits fell from 2,434,285 to 2,416,086) has the Cyprus Tourism Organization (CTO) considering new approaches to selling its tourism product. A focus on sports tourism is one approach being weighed.

In 2003 the CTO adopted a tourism development plan, and accompanying strategy for implementation, with 2010 as the target date. The plan identified competitive and recreational sports as likely contributors to the achievement of its five objectives: (a) increasing per-tourist expenditure, (b) improving winter season tourism, (c) extending tourists’ stays in Cyprus, (d) increasing repeat visits, and (e) increasing the number of tourist arrivals in Cyprus. The CTO’s plan called specifically for the development of sports services and sports-related human resources and for the organization of sports events.

Research by Papanikos (2002) indicates that countries interested in expanding sports tourism must carefully consider how to go about that task. Building new facilities is not necessarily the right approach to establish a sports tourism market, and Papanikos advises officials like those in Cyprus to pursue extensive research before investing in the sports tourism industry (2002). Thus the CTO, prior to creating its 2010 plan, completed a SWOT analysis—an assessment of strengths, weaknesses, opportunities, and threats characterizing an enterprise—to evaluate sports tourism’s appropriateness as a major pillar of the strategic plan for tourism in Cyprus (Kartakoullis & Karlis, 2002). The analysis by Kartakoullis and Karlis (2002) indicated that potential existed for developing sports tourism in Cyprus. Strengths and opportunities were plentiful, and the 2004 Olympics in Athens, Greece, would provide a means to educate the international community about Cyprus’s sports tourism potential. The analysis also noted, however, that positioning Cyprus as a sports tourism destination would demand the collaboration of the nation’s tourism and sports industries and experts, given certain internal weaknesses such as lack of existing expertise in sports tourism. Organizations assuming a role in developing sports tourism in Cyprus would be able to administer services effectively only if proper strategic management were provided.

Kartakoullis and Karlis’s SWOT analysis (2002) was the initial study concerning sports tourism in Cyprus. It argued that Cyprus has all the necessary elements of a sports tourism destination, and it comprised a first guide for the CTO and the national government, as well as interested private tourism and sports groups. None of these players had a formal policy on sports tourism, and all were likely to be needed to administer future sports tourism services. A series of issues was identified that the three players would need to consider. The present study grew from those identified issues and represents expanded research on sports tourism’s potential in Cyprus, as called for by Kartakoullis and Karlis.

To suit the present study’s purpose, the definition of sports tourism offered by Gibson, Attle, and Yiannakis (1997)—namely, that sports tourism is travel undertaken in order to participate in recreational or competitive sports—was expanded. A third type of sports tourism, leisure sports tourism, was added. Sports tourism here, then, refers to travel for reasons related to (a) elite or amateur athletic competition, training, or other related preparation; (b) recreation sports, defined as participation in competitive sports without trophy rewards; or (c) leisure sports, defined as play or pastimes involving a sports activity. The study examined the sports tourism experiences of all three types that international visitors to Cyprus self-reported during interviews. Specific objectives of the study were to assess the purposes of tourist visits to Cyprus; to identify sports activities in which tourists participate while in Cyprus; and to explore tourists’ intentions concerning future sports tourism visits to Cyprus.

Procedures

To begin the study, we obtained from the Department of Civil Aviation in Cyprus a list of July and August 2005 departures from the country’s two main international airports, which are in the cities of Larnaca and Pafos. The destinations of the departing flights included the United Kingdom, countries in western Europe, countries in eastern Europe, countries in the Middle East, and other destinations. The four regions and catchall category (other destinations) supplied categories used to ensure that a representative sample of departing tourists would be interviewed. Using the list of departures from the two airports, we prepared a timetable for data collection, covering all destination categories at various times of the day and night.

Keeping to this timetable, a team of trained interviewers conducted 489 face-to-face interviews in Larnaca and 313 in Pafos. An interview lasted approximately 5–10 minutes as the respondent prepared to take a departing flight. The interviewers asked participants a series of quantitative questions, including basic demographic questions as well as questions about the current trip to Cyprus. Respondents were asked about (a) the purpose of their travel to Cyprus, (b) any sports activities they participated in while in Cyprus, and (c) whether their intention was to visit Cyprus again for sports-related purposes. The questionnaire was designed to generate basic descriptive statistics in the form of frequency counts and percentages.

Results

Demographic Characteristics

Males comprised a slight majority of respondents, 51% (n = 407); females comprised 49% (n = 395). The occupational status of the majority of the respondents—65% , or 511 respondents—was white-collar professional or white-collar personnel (see Table 1). British tourists have long been a mainstay of Cyprus’s hospitality industry. In this study, respondents from the United Kingdom, at 62.5% of the sample, characteristically outnumbered those from other nations. German tourists were next most numerous, comprising 8.6% (see Table 2).

Table 1

Respondents’ Occupation Status, Most Represented to Least Represented

Occupation Status Number of respondents indicating this status Percentage of respondents indicating this status
White-collar personnel 374 47
White-collar professional 142 18
Blue-collar worker 139 17
Student 81 10
Retired 35 4
Homemaker 31 4

Table 2

Respondents’ Country of Residence, Most Represented to Least Represented

Country Number of respondents (N = 802) Percentage of all respondents
United Kingdom 501 62.5
Germany 69 8.6
Sweden 48 6.0
Norway 42 5.2
Ireland 27 3.4
Greece 19 2.4
Netherlands 17 2.1
Switzerland 13 1.6
Israel 11 1.4
Denmark 10 1.2
Hungary 9 1.1
Russia 9 1.1
Belarus 3 0.4
Austria 2 0.2
Bahrain 2 0.2
Canada 2 0.2
Hong Kong 2 0.2
Iran 2 0.2
Japan 2 0.2
Jordan 2 0.2
Oman 2 0.2
United States 2 0.2
Other countries 6 0.7

Current Trip to Cyprus

The largest percentages of tourists interviewed for the study had secured accommodations (for the main part of their current stay in Cyprus) in the tourist destinations Pafos (sometimes spelled Paphos) (39%) and Ammohostos (38%) (see Table 3). The next most popular sites for accommodations were Limassol (sometimes called Lemesos) (11%) and Larnaca (9%). Three percent of those interviewed had stayed mainly in the capital city of Nicosia, which, while it is a business center, is not widely considered a place for tourists (see Table 3). Fully half of the respondents had stayed 6 to 10 days in Cyprus; another 29% had spent 11 to 15 days on the island (see Table 4).

Table 3

Site of Respondents’ Main Accommodations in Cyprus, Most to Least Popular

City Number of respondents (N = 802) with accommodations in city Percentage of respondents with accommodations in city
Pafos (Paphos) 312 39.0
Ammohostos 306 38.0
Limassol (Lemesos) 92 11.0
Larnaca 71 9.0
Nicosia 21 3.0

Table 4

Duration of Respondents’ Visits to Cyprus, in Days

Days Number of respondents (N = 802) Percentage of respondents
1-5 126 16.0
6-10 398 50.0
11-15 232 29.0
More than 15 46 6.0

The respondents were asked the reason for their current travel to Cyprus and were allowed to offer more than one reason. Including the multiple responses, 864 reasons for visiting Cyprus were recorded for the 802 respondents (see Table 5). The most common reason was tourism/recreation; 87.8%, or 704 respondents, said they traveled to Cyprus for that purpose (see Table 5). A reason involving sports tourism specifically was given by 16 respondents, or 2.0%. (The breakdown by type of sports tourism was as follows: recreation sports tourism, 1.2%, and competition sports tourism, 0.8%, with 0.4% of the latter representing preparation for competition and 0.4% representing actual participation in competition.)

Of the 16 respondents who traveled to Cyprus for sports tourism purposes, 13 were male and 3 were female (see Table 6). The largest percentage of people visiting Cyprus in order to pursue sports-related activities were aged 20–29 years; the next largest group of sports tourists were aged 60 or more. All respondents indicating they had visited Cyprus for sports tourism purposes were from western Europe (see Table 8). Those who came because of sports competitions stayed in Cyprus 11–15 days, whilst those who came to prepare for competition spent 6–10 days (see Table 8).

Table 5

Purpose of Respondents’ Current Travel to Cyprus, Most to Least Common (Sports-Related Purposes Shaded)

Number of respondents stating this purpose Percentage of all respondents
Tourism/recreation 704 87.8
Business 72 9.0
Visiting relatives 32 4.0
Attending a wedding 20 2.5
Visiting friends 18 2.2
Recreation sports tourism 10 1.2%
Competition sports tourism—actual competition 3 0.4%
Competition sports tourism—preparation 3 0.4%
Attending a funeral 1 0.1%
Honeymooning 1 0.1%

Note.Because respondents were not limited to a single purpose for travel, 864 responses were recorded for the interview item on purpose of travel. To obtain the percentages in the column headed “Percentage of all respondents,” the number of respondents stating a particular purpose (middle column) was divided by 802 (the sample size). The right-hand column entries total 107.7% (= 864/802). For the same reason, entries in the middle column of Tables 6-13 do not equal 802 and entries in the tables’ right-hand columns do not equal 100%.

Table 6

Purpose of Respondents’ Current Travel to Cyprus, by Gender

Male Female
Number of male respondents stating this purpose (n = 407) Percentage of male respondents stating this purpose Number of female respondents stating this purpose Percentage of female respondents stating this purpose
Tourism/recreation 354 87.0 350 88.6
Business 52 12.8 20 5.1
Visiting relatives 14 3.4 18 4.6
Attending a wedding 10 2.5 10 2.5
Visiting friends 5 1.2 13 3.3
Recreation sports tourism 8 2.0 2 0.5
Competition sports tourism—actual competition 2 0.5 1 0.3
Competition sports tourism—preparation 3 0.7 0 0.0
Attending a funeral 0 0.0 1 0.3
Honeymooning 1 0.2 0 0.0

Note. See note for Table 5.

Table 7

Purpose of Respondents’ Current Travel, by Age (in Years), as Percentage of Respondents in Each Age Group n

Percentage of those < 20 years old (n = 43) stating this purpose Percentage of those 20–29 years old (n = 210) stating this purpose Percentage of those 30–39 years old (n = 233) stating this purpose Percentage of those years old 40–49 (n = 168) stating this purpose Percentage of those years old 50–59 (n = 96) stating this purpose Percentage of those > 60 years old (n = 52) stating this purpose
Tourism/recreation 90.7 88.1 88.4 83.9 89.6 90.4
Business 2.3 12.4 8.2 11.3 5.2 3.8
Visiting relatives 7.0 1.9 2.6 6.5 5.2 5.8
Attending a wedding 0.0 1.9 5.6 0.6 2.1 0.0
Visiting friends 0.0 1.9 2.1 3.6 1.0 3.8
Recreation sports 0.0 2.4 1.3 0.0 1.0 1.9
Competition sports tourism—actual competition 0.0 1.4 0.0 0.0 0.0 0.0
Competition sports tourism—preparation 0.0 1.0 0.0 0.6 0.0 0.0
Attending a funeral 0.0 0.0 0.4 0.0 0.0 0.0
Honeymooning 0.0 0.0 0.4 0.0 0.0 0.0

Table 8

Purpose of Respondents’ Travel, by Country of Residence, as Percentage of n

Percent of those from the United Kingdom (n = 501) stating this purpose Percent of those from Western Europe (n = 249) stating this purpose Percent of those from Eastern Europe (n = 23) stating this purpose Percent of those from the Middle East (n = 19) stating this purpose Percent of those from other countries (n = 10) stating this purpose
Tourism/recreation 86.45 92.0 95.7 68.4 70
Business 8.6 7.6 8.7 21.1 40
Visiting relatives 4.4 2.8 4.3 5.3 10
Attending a wedding 3.4 1.2 0.0 0.0 0.0
Visiting friends 3.4 0.0 0.0 5.3 0.0
Recreation sports 1.4 1.2 0.0 0.0 0.0
Competition sports tourism—actual competition 0.6 0.0 0.0 0.0 0.0
Competition sports tourism—preparation 0.6 0.0 0.0 0.0 0.0
Attending a funeral 0.2 0.0 0.0 0.0 0.0
Honeymooning 0.2 0.0 0.0 0.0 0.0

Note. See note for Table 5.

Respondents’ Sports Activities While Visiting Cyprus

Most respondents (85.8%, or 688 individuals) indicated they had participated in some type of sports experience during their visit to Cyprus (see Table 9); 114 respondents said they did not participate in any type of sports in Cyprus (14.2%). Swimming was most widely participated in (by 82.9%, or 665 respondents), followed by water sports (24.7%, or 198 respondents), and soccer (7.2%, or 58 respondents). For males and females alike, swimming and water sports were the top two sports pursued. In the subsample of females, however, it was beach volleyball rather than soccer that was the third most popular sports activity.

Visitors from the United Kingdom and western Europe tended to participate more in sports activities while in Cyprus than did visitors from eastern Europe or the Middle East (see Table 11). Visitors who stayed mainly in Ammohostos and Pafos were most likely to have participated in sports during their time in Cyprus; those staying in Nicosia were least likely to have (see Table 12). Finally, those respondents staying in Cyprus for more than six days showed the highest rate of sports participation during a visit (see Table 13).

Table 9

Sports the Respondents Participated in While in Cyprus, Most to Least Commonly

Number of respondents stating this sport Percentage of respondents stating this sport
Swimming 665 82.9
Water sports 198 24.7
No sports activity 114 14.2
Soccer 58 7.2
Cycling 56 7.0
Beach volleyball 52 6.5
Tennis 51 6.4
Orienteering 34 4.2
Golf 12 1.5
Jogging 10 1.2
Gymnastics 6 0.7
Aerobic exercise 4 0.5
Fishing 3 0.4
Bungee jumping 2 0.2
Equestrian sports 2 0.2
Miniature golf 2 0.2
Parachuting 2 0.2
Bowling 1 0.1
Go-Karting 1 0.1
Diving 1 0.1
Judo 1 0.1
Karate 1 0.1
Table Tennis 1 0.1

Note. See note for Table 5.

Table 10

Sports the Respondents Participated in While in Cyprus, by Gender, as a Percentage

Percentage of males stating this sport Percentage of females stating this sport
Swimming 80.8 85.1
Water sports 27.5 21.8
No sports activity 14.7 13.7
Soccer 13.8 0.5
Cycling 8.4 5.6
Beach volleyball 4.2 8.9
Tennis 7.9 4.8
Orienteering 3.9 4.6
Golf 2.5 0.5
Jogging 1.5 1.0
Gymnastics 0.7 0.8
Aerobic exercise 0.0 1.0
Fishing 0.5 0.3
Bungee jumping 0.2 0.3
Equestrian sports 0.0 0.5
Miniature golf 0.2 0.3
Parachuting 0.0 0.5
Bowling 0.0 0.3
Go-Karting 0.2 0.0
Diving 0.2 0.0
Judo 0.2 0.0
Karate 0.2 0.0
Table Tennis 0.2 0.0

Note. See note for Table 5.

Table 11

Sports the Respondents Participated in While in Cyprus, by Country of Residence, as a Percentage

Percentage of visitors from United Kingdom stating this sport Percentage of visitors from Western Europe stating this sport Percentage of visitors from Eastern Europe stating this sport Percentage of visitors from Middle East stating this sport Percentage of visitors from other countries stating this sport
Swimming 82.2 84.3 82.6 73.7 100.0
Water sports 26.7 22.1 21.7 21.1 0.0
No sports activity 15.0 12.4 17.4 21.1 0.0
Soccer 9.6 4.0 0.0 0.0 0.0
Cycling 8.6 4.8 4.3 0.0 0.0
Beach volleyball 8.4 3.6 4.3 0.0 0.0
Tennis 5.6 8.4 0.0 10.5 0.0
Orienteering 5.2 2.4 0.0 5.3 10.0
Golf 1.6 1.6 0.0 0.0 0.0
Jogging 0.8 2.0 0.0 5.3 0.0
Gymnastics 0.8 0.4 0.0 5.3 0.0
Aerobic exercise 0.2 0.8 4.3 0.0 0.0
Fishing 0.4 0.4 0.0 0.0 0.0
Bungee jumping 0.4 0.0 0.0 0.0 0.0
Equestrian sports 0.2 0.4 0.0 0.0 0.0
Miniature golf 0.2 0.4 0.0 0.0 0.0
Parachuting 0.2 0.4 0.0 0.0 0.0
Bowling 0.2 0.0 0.0 0.0 0.0
Go-Karting 0.0 0.4 0.0 0.0 0.0
Diving 0.2 0.0 0.0 0.0 0.0
Judo 0.0 0.4 0.0 0.0 0.0
Karate 0.2 0.0 0.0 0.0 0.0
Table Tennis 0.0 0.4 0.0 0.0 0.0

Note. See note for Table 5.

Table 12

Sports Participated in While in Cyprus, by Site of Main Accommodations, as a Percentage

Percentage of visitors to Pafos stating this sport Percentage of visitors to Ammohostos stating this sport Percentage of visitors to Limassol stating this sport Percentage of visitors to Larnaca stating this sport Percentage of visitors to Nicosia stating this sport
Swimming 81.1 90.5 76.1 77.5 47.6
Water sports 21.2 30.1 25.0 21.1 9.5
No sports activity 14.1 8.8 21.7 19.7 42.9
Soccer 12.5 3.6 4.3 4.2 4.8
Cycling 13.5 1.3 5.4 7.0 0.0
Beach volleyball 11.5 2.6 8.7 0.0 0.0
Tennis 9.9 3.6 5.4 4.2 4.8
Orienteering 5.8 3.6 1.1 5.6 0.0
Golf 2.6 0.3 1.1 1.4 4.8
Jogging 1.0 2.0 1.1 0.0 0.0
Gymnastics 0.3 0.7 1.1 1.4 4.8
Aerobic exercise 0.0 1.0 0.0 1.4 0.0
Fishing 0.0 0.7 0.0 1.4 0.0
Bungee jumping 0.0 0.7 0.0 0.0 0.0
Equestrian sports 0.0 0.3 1.1 0.0 0.0
Miniature golf 0.0 0.7 0.0 0.0 0.0
Parachuting 0.0 0.7 0.0 0.0 0.0
Bowling 0.0 0.0 0.0 1.4 0.0
Go-Karting 0.0 0.0 1.1 0.0 0.0
Diving 0.0 0.3 0.0 0.0 0.0
Judo 0.3 0.0 0.0 0.0 0.0
Karate 0.0 0.0 1.1 0.0 0.0
Table Tennis 0.0 0.3 0.0 0.0 0.0

Note. See note for Table 5.

Table 13

Respondents’ Participation in Sport Activities by Duration of Stay (in Days), as a Percentage

Percentage of visitors staying 1–5 days stating this sport Percentage of visitors staying 6–10 days stating this sport Percentage of visitors staying 11–15 days stating this sport Percentage of visitors staying more than 15 days stating this sport
Swimming 69.8 86.4 84.1 82.6
Water sports 17.5 23.6 30.6 23.9
No sports activity 28.6 11.3 11.6 13.0
Soccer 11.1 7.5 4.7 6.5
Cycling 4.8 8.8 4.7 8.7
Beach volleyball 10.3 7.8 3.4 0.0
Tennis 4.8 6.8 7.3 2.2
Orienteering 4.8 4.0 3.0 10.9
Golf 2.4 1.8 0.9 0.0
Jogging 0.8 1.5 1.3 0.0
Gymnastics 1.6 0.8 0.4 0.0
Aerobic exercise 0.8 0.5 0.4 0.0
Fishing 0.0 0.3 0.4 2.2
Bungee jumping 0.0 0.3 0.4 0.0
Equestrian sports 0.0 0.3 0.4 0.0
Miniature golf 0.0 0.3 0.4 0.0
Parachuting 0.0 0.5 0.0 0.0
Bowling 0.8 0.0 0.0 0.0
Go-Karting 0.0 0.3 0.0 0.0
Diving 0.0 0.0 0.4 0.0
Judo 0.0 0.3 0.0 0.0
Karate 0.0 0.3 0.0 0.0
Table Tennis 0.8 0.0 0.0 0.0

Note. See note for Table 5.

Intention to Visit Cyprus Again to Participate in Sports Tourism

The respondents were asked during their interviews whether it was their intent to visit Cyprus again in order to participate in sports tourism; 87% said they did intend to do so, and 13% indicated they had no intention of returning to Cyprus to participate in sports activity at any future time.

Discussion

A major limitation of the study was that data were collected only during the summer months. Data collected in the winter season might generate different results, because Cyprus also features mountainous regions, like Troodos, where winter sports like cross-country and alpine skiing, snowboarding, and snowshoeing are available. Conducting a similar airport-interview study during the winter months would be interesting.

In any case, the summer study results indicate that few sports tourists come to Cyprus to pursue either the competition (whether actual competition or training or preparation for competition) or recreation types of sports. And it certainly is no surprise that most sports tourists in Cyprus are leisure sports tourists. The climate and the beaches of Cyprus provide ample opportunities to pursue leisure swimming and water sports, and these were indeed the sports activities most widely pursued by the respondents in our study.

The study generated information that may be useful for the further development of sports tourism in Cyprus. For example, the data show that sports tourists tend to come to Cyprus from the United Kingdom and western Europe. As Weed and Bull have suggested (2004), the proximity of Europe to Cyprus should support growth in sports tourism by Europeans in Cyprus. The CTO’s sports tourism marketing strategies in Europe, then, might promote Cyprus as a sports tourism destination. (The marketing strategies for eastern Europe and the Middle East might follow suit.)

Particular CTO campaigns targeting Europe and other regions should address the fact (supported by our data) that many who visit Cyprus engage in leisure sports activities rather than competitive or recreation ones. More competitive and recreation sports tourists might be drawn to the country if its resources for competitive and recreation sports tourism were actively marketed. The experience of the United Kingdom’s Olympic team, which trained in Cyprus prior to the Athens Games, offers a starting place. After the Games had concluded, the British Olympic performance manager, Richard Simmons, commented that “We made the right decision to choose Cyprus as not just our training base for the Athens Olympic Games but also our warm weather training centre of operations for at least the next ten years. Cyprus now offers great training facilities for a huge range of sports, and is blessed with wonderful weather and a superb environment. Athletes and coaches from whatever the sport and whatever level could not choose a better place” (Simmons, 2005).

The benefits of a plan to build sports tourism in Cyprus would extend to the nation’s citizens as well as tourists (Hall, 2000). Whether or not new sports facilities are part of it, such a plan can be expected to point the way to development of local economies as well as to citizens’ increased use of improved sports services and available facilities. Our data show that most respondents say they would return to Cyprus specifically for sports tourism experiences. There is, then, potential for Cyprus to become a sports tourism destination, enjoying the financial impact such tourism can bring. The Cyprus Tourism Organization might consider moving in a direction that develops and broadens Cyprus’s sports tourism role.

References

Cyprus Tourism Organization. (2003). Tourism development strategy and implementation plan: 2003–2010. (Available from the Cyprus Tourism Organisation, Lemesou Ave. 19, P. O. Box 24535, CY 1390, Lefkosia, Cyprus)

Cyprus Tourism Organization. (2007). Annual report 2007. (Available from the Cyprus Tourism Organisation, Lemesou Ave. 19, P. O. Box 24535, CY 1390, Lefkosia, Cyprus)

Gibson, H. (1998). Sport tourism: A critical analysis of research. Sport Management Review, 1(1): 45–76.

Gibson, H. J., Attle, S., & Yiannakis, A. (1997). Segmenting the active tourist market: A life span perspective. Journal of Vacation Marketing, 4(1): 52–64.

Hall, C. M. (2000). Tourism planning. New York: Prentice Hall.

Hinch, T., Jackson, E. L., Hudson, S., & Walker, G. (2005). Leisure constraint theory and sport tourism. Sport in Society, 8(2): 142–163.

Hudson, S. (2003). Sport and adventure tourism. New York: Haworth Press.

Karlis, G. (2006, September). Assessing the needs of “sport volunteer tourists” at the Olympic Games: Implications for administrators of mega sport events. Keynote address presented at the 14th congress of the European Association for Sport Management, Nicosia, Cyprus.

Kartakoullis, N. L., & Karlis, G. (2002). Developing Cyprus as a sport tourism destination: The results of a SWOT analysis. Journal of Sport Tourism, 7(4): 1–16.

Neirotti, L. D. (2005). Sport tourism markets. In J. E. S. Higham (Ed.), Sport tourism destinations: Issues, opportunities and analysis (pp. 1–16). Oxford, Oxfordshire, UK: Elsevier.

Papanikos, G. (2002, May). Tourism in Greece. Paper presented at the meeting of the OKE (Economic and Social Council of Greece), Ottawa, Ontario, Canada.

Simmons, R. (2005, February). Choosing a pre–Olympic Games training destination. Paper presented at the meeting of the Cyprus Tourism Organization, Nicosia, Cyprus.

Weed, M., & Bull C. (2004). Sports tourism: Participants, policy and providers. New York: Elsevier Butterworth Heinemann.

2016-10-12T14:57:35-05:00January 8th, 2009|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Sports Tourism in Cyprus: A Study of International Visitors
Read More

Characteristics Contributing to the Success of a Sports Coach

Submitted by: Jefrey L. Frost – United States Sports Academy

Abstract

Identifying particular characteristics (qualities and abilities) of successful sports coaches could offer other coaches help in improving their performance. Toward this end, 15 high school coaches completed a survey on 17 possible such characteristics, ranking 5 of them above the rest (≥ 90th percentile): quality of practice, communicating with athletes, motivating athletes, developing athletes’ sports skills, and possessing knowledge of the sport. Coaches seeking to enhance their success might focus on these characteristics.

(more…)

2016-09-30T08:25:51-05:00January 7th, 2009|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science|Comments Off on Characteristics Contributing to the Success of a Sports Coach
Read More
Go to Top