The Formula One (F-1) Grand Prix has evolved into a global sport that generates over $4 billion in business transactions annually (Financial Times, 2012). The popularity of this sport is reflected in the television audience viewership as its events are one of the most widely watched spectator sports, with nearly 600 million viewers per year. Being a host city for an F-1 Grand Prix event is a highly coveted commission, but paying the hosting fees can be a daunting burden; in fact, race hosting fees alone generated $568 million for F-1 in 2010 (The Formula Money Report, 2011). Because of the cost of hosting, among other reasons, it has been reported that cities hosting F-1 events have had difficulty in making a profit (The Formula Money Report, 2011). For example, an agreement was reached for a Grand Prix event to be held in Shanghai, China, in 2004. To host this race, the Shanghai International Circuit was built at a cost of $450 million. The building costs, along with race hosting fees and operating costs, were so high that the Shanghai governing authority cannot recover the deficit until 2014 (Financial Times, 2012). Other hosting cities have experienced similar circumstances.

Improving financial achievement would be necessary for the F-1 Grand Prix events to continue to be attractive to current and potential event host cities. Major income sources for a Grand Prix event host city usually include event ticket sales that typically range from $100 to $2,000 per ticket, sponsorship, and broadcasting fees. To a great extent, sponsorship income is a function of the size of live and televised event audiences. In previous research, it has been suggested that enhancing spectator involvement would be necessary to boost revenue generation in both of these areas (Mullin, Hardy, & Sutton, 2007; Pease & Zhang, 2001; Trail & James, 2001). As sport consumption behavior is motivated, channeled, and sustained by social reasons (Cianfrone, Zhang, & Ko, 2011), it would be most constructive for the event managers and marketers of the host city to have an in-depth understanding of the dynamic nature of spectator motivation for attending an F-1 event.

Despite the fact that the F-1 event has the potential to become a microcosm of the economy for the host community, few scholars have paid attention to examining the phenomena associated with F-1 events (see e.g., Baum & Lockstone, 2007; Fairley, Tyler, Kellett, & D’Elia, 2011; Farrelly, Quester, & Burton, 1997; Hall, O’Mahony, & Vieceli, 2010; Quester & Farrelly, 1998). For example, Farrelly et al. (1997) proposed a strategy to incorporate sport sponsorship into the integrated marketing communication for the F-1 event. Quester and Farrelly examined spectator perceptions of F-1 event sponsorship through a longitudinal study and found that spectators displayed high recall rates for sponsors when congruency existed between the sponsor’s domain of activity and the F-1 race.

Fairley et al. (2011) conducted a case study of the F-1 Australian Grand Prix to examine the sustainability of the event; based on the research findings, these researchers suggested that the event organizers consider adopting the triple bottom-line approach, by including economic, social, and environmental impacts in their investigation before bidding to host an event. Baum and Lockstone (2007) developed a framework to better utilize volunteers during a mega-event to maximize economic benefits for the host community. Hall et al. (2010) developed a theoretical model for studying key factors affecting attendance at major sport events. Although the model has been empirically verified, the predictors in the model are related to environmental (servicescape) and attitudinal variables (emotion). In brief, although findings in previous studies shed some light on event management of F-1 races, to date we have not found a study in which sport consumption factors are specifically examined.

In recent empirical studies it has been found that consumer sociomotivation is one of the most important factors that influence spectatorship (Funk, Beaton, & Alexandris, 2012; Pease & Zhang, 2001; Trail, Fink, & Anderson, 2003; Wann, 1995). In the majority of previous studies researchers have primarily placed reliance upon the multiattribute aspects of motivation (Funk, Mahony, & Ridinger, 2002; Lough & Kim, 2004), which resulted in (a) identifying a plethora of motive dimensions; for example, Trail and James (2011) identified just under 40 motive dimensions, (b) explaining a low level of variance of between 10-20% in sport consumption behavior, and (c) inconsistent findings regarding salient factors in motivation of sport consumption behavior. To address these limitations, Funk et al. (2012) conducted a theory-based study using self-determination theory (SDT; Deci & Ryan, 1985) as a framework to examine key motivation factors that explain various sport consumption behaviors and found that over 60% of the variance could be explained in game attendance, media usage, and licensed merchandise purchase. Funk et al. suggested that the use of a well-established theory, coupled with incorporating multiple consumption variables into their 2012 study, provided a more robust understanding of sport consumption behavior. However, we have identified a limitation associated with the utilization of SDT in the study conducted by Funk et al. (2012), in that the motive dimensions were categorized into autonomy and control, based on the SDT framework. Instead of adopting measures derived from the SDT framework, where the items were not directly relevant to the sport marketing context, the authors adapted items from the SPEED (i.e., socialization, performance, esteem, excitement, and diversion) motive dimensions, which suggests to us a need to adopt context-specific theories and measures to study spectator motivations.

More than two decades ago, Sloan (1989) proposed five theoretical dimensions associated with sport consumer motivation. These dimensions are entertainment theory, achievement-seeking theory, catharsis theory, salubrious-effects theory, and stimulation-seeking theory. Sloan’s theoretical categorization has been empirically verified in various sport marketing contexts, such as the National Basketball Association (NBA; Pease & Zhang, 2001) and National Hockey League (NHL; Zhang et al., 2001). Although it has been reported in previous studies that two of the categories (i.e., catharsis and stimulation seeking) were not empirically distinct, overall, Sloan’s theory captured key motive dimensions affecting sport consumption behavior. Whereas Sloan’s motive categories have been effectively adapted in various sport settings, to date we have not found a study in which the theory has been applied to the F-1 race setting. Therefore, in the current study we examined the relationship between spectator social motivations and sport consumption behavior in terms of repurchase intentions and word-of-mouth referrals of F-1 events.

Review of Literature and Hypotheses Development

Spectator Motivation

According to Evans, Jamal, and Foxall (2009), motivation is defined as “the driving force within individuals that moves them to take a particular action” (p. 6). This energizing force is typically generated as a result of having an unfulfilled need. As such, this concept has been well documented as one of the psychological variables that most affect sport consumption behaviors that include, but are not limited to, sport event attendance (Pease & Zhang, 2001), media consumption (Byon, Cottingham, & Carroll, 2010), and purchase of licensed merchandise (Funk et al., 2012). Research inquiries have resulted in the development of various scales, including the Sport Fan Motivation Scale (SFMS; Wann, 1995), the Spectator Motivation Scale (SMS; Pease & Zhang, 2001), the Motivation Scale for Sport Consumption (MSSC; Trail & James, 2001), and the Sport Interest Inventory (SII; Funk et al., 2002; Funk, Ridinger, & Moorman, 2003). These scales have generally been developed in the context of professional and intercollegiate sports. Recently, motivation studies have been applied to a broader spectrum, including mixed martial arts (Andrew, Kim, O’Neal, Greenwell, & James, 2009), soccer (Mehus, 2005), disability sports (Byon, Zhang, & Connaughton, 2010), and sport video games (Cianfrone et al., 2011; Kim & Ross, 2006).

Through these studies numerous motive factors have been conceptualized, which can be categorized into four overarching theoretical categories that are consistent with Sloan’s (1989) theory: (a) entertainment theory, (b) achievement-seeking theory, (c) catharsis theory, and (d) salubrious-effects theory. In entertainment theory it is postulated that sport consumers are attracted to a consumption activity for seeking pleasure and happiness. The aesthetic aspects of the F-1 driver’s skill and appearance even make the sport an art form for spectators. In achievement seeking theory it is suggested that sport consumers are motivated to consume a sport because they are seeking vicarious achievement by expressing their bond and attachment to the sport (e.g., an F-1 driver or F-1 team). It is suggested that a sport consumer seeks the vicarious achievement to either enhance or protect his/her self-esteem. The names coined for these phenomena are basking in reflected glory (BIRGing; Cialdini et al., 1976) – an ego-enhancement mechanism – and cutting off reflected failure (CORFing; Snyder, Lassegard, & Ford, 1986) – a self-preservation mechanism. The impact of the BIRGing and CORFing concepts are well established in the sport fan identification literature (e.g., Trail et al., 2012). In catharsis theory it is posited that achieving feelings of catharsis or release of aggression is a motivation for some to consume a physical and aggressive sport, such as hockey (Lee, Trail, & Anderson, 2008) and mixed martial arts (Andrew et al., 2009). When they conducted a survey with spectators of NHL, Lee et al. (2008) found that spectators were attracted to the games by aggressive play. Oftentimes, Ultimate Fighting Championship event organizers emphasize the aspects of catharsis, violence, and aggression in their marketing promotions. Relatedly, the F-1 event is also known for speed, aggressive driving, and noise, all of which would attract spectators who are seeking catharsis to the event. Finally, the in the salubrious-effects theory it is purported that spectators consume a sport for its pleasure and for their own mental well-being. Spectators use game attendance as an opportunity to escape from daily routines and boredom. These four theoretical categories associated with sport consumer motivation have been empirically found to explain fan consumption activities in diverse sport contexts (e.g., Pease & Zhang, 2001; Zhang et al., 2001).

Spectator Consumption Behavior

Oliver (1999) proposed that there are four stages of consumption loyalty: cognitive loyalty, affective loyalty, conative loyalty, and action loyalty. Of those, action loyalty is the hardest to measure because consumer surveys cannot be conducted at the exact moment of purchase. Even if the consumer was intercepted immediately after the purchase was complete, that consumption would already be in the past and, therefore, the action is complete. Thus, conative loyalty (i.e., behavioral intentions) is regarded as an alternative method to assess customer purchase behavior. Ajzen (2005) operationalized behavioral intention as an indication of an individual’s willingness toward a given task. Numerous researchers have provided the empirical support for this proposition by finding that behavioral intentions are good predictors of actual consumption behavior (e.g., Zeithaml, Berry, & Parasuraman, 1996). In the sport consumption context, behavioral intentions are theorized as a multidimensional concept that includes reattendance intention, media consumptive intention, and buying intention of licensed merchandise (Byon, Cottingham, & Carroll, 2010; Fink, Trail, & Anderson, 2002; Kwon, Trail, & James, 2007). In the business literature, a frequently used construct assessing behavioral intentions is word-of-mouth (Brown & Reingen, 1987; East, Hammond, & Lomax, 2008), which is an informal communication between customers concerning the evaluation of products and services. Previous researchers have found that “positive word-of-mouth was 700% more effective than newspaper advertising, 400% more effective than personal selling, and 200% more effective than radio advertising in getting consumers to change brands” (Severt, Wang, Chen, & Breiter, 2007, p. 401), which suggests the powerful influence of word-of-mouth referrals on consumer behavior. East et al. (2008) further studied the effect of word-of-mouth by examining the differential impact of positive word-of-mouth (PWOM) and negative word-of-mouth (NWOM). Through adopting a mixed research method using both experiment and survey, the researchers found that the effect of PWOM on purchase behavior was more significant than that of NWOM. Findings of previous researchers also suggest that satisfied customers tend more than do unsatisfied customers to engage in referring services and products they have used to their friends and families. This notion has been well established in marketing, tourism, and hospitality literature (e.g., Han, Back, & Barrett, 2009; Liu & Jang, 2009). Although word-of-mouth has rarely been examined in a sport marketing context, we assumed that sport consumers would convey positive word-of-mouth referral regarding their experience with sport products and services to their reference groups and/or online community. Therefore, in this study, sport consumption was operationalized as two behavioral intentions: (a) repurchase intention of F-1 events, and (b) word-of-mouth referral about F-1 events. This two-pronged conceptualization of behavioral intentions is also consistent with that found in the general literature on marketing, tourism, and hospitality (Han et al., 2009; Liu & Jang, 2009).

Impact of Spectator Motivation on Consumption Behavior

The relationship between sport consumer motivation and various sport consumption behaviors is well documented in sport marketing literature (Andrew et al., 2009; Funk et al., 2012; Funk, Filo, Beaton, & Pritchard, 2009; Mahony, Nakazawa, Funk, James, & Gladden, 2002; Pease & Zhang, 2001; Zhang et al., 1997). In an empirical study involving spectators of NBA, Pease and Zhang (2001) found that salubrious effects and entertainment were positively related to game attendance variables. Funk et al. (2009) found that performance, esteem, and excitement were motivational factors predicting attendance at Australian football events. Mahony et al. (2002) found that 15% of the variance in attendance among spectators at Japanese professional soccer league games was explained by several motivation factors. Studying minor league hockey spectators, Zhang et al. (1997) noted that achievement seeking and salubrious effects were highly predictive of intended attendance at future events. Funk et al. (2012) found that five motive factors of socialization, performance, esteem, excitement, and diversion significantly predicted attendance, media usage, purchase, and uniform-wearing behaviors in the context of Australian football.

In the context of mixed martial arts events, Kim, Greenwell, Andrew, Lee, and Mahony (2008) found that vicarious achievement, national pride, and drama factors were motives salient to sport consumption. In a follow-up study, Andrew et al. (2009) identified violence, aesthetics, drama, and knowledge as factors serving as significant predictors of consuming mixed martial arts events. Although sport marketing researchers have rarely conducted studies devoted to examining the effect of motivation on word-of-mouth referrals, the impact of motivation on referrals by word-of-mouth has been well established in closely related disciplines, such as marketing, tourism, and hospitality (e.g., Hung & Petrick, 2010; Yoon & Uysal, 2005). For instance, in their study of tourism, Yoon and Uysal found that travelers’ push motivation (intangible or intrinsic desires of the individual traveler to escape, relax, and so on) was positively related to revisit intentions and word-of-mouth information about the destination. Surveying cruise passengers, Hung and Petrick (2010) found that travel motivations positively influenced intentions to go on another cruise and cruise referrals (i.e., by word-of-mouth). In brief, previous research findings have been consistent in showing that consumer motivations positively impact their consumption behaviors in the areas of repurchase intentions and word-of-mouth referrals and information. On the basis of these findings, in the current study we anticipated that Sloan’s (1989) motive factors would positively influence the consumption behavior of spectators of F-1 races; specifically, we tested the following two hypotheses in this study:
Hypothesis 1: Spectator motive factors will be positively associated with repurchase intentions of F-1 event spectators.
Hypothesis 2: Spectator motive factors will be positively associated with intentions to share with family and friends by word-of-mouth their enjoyment of being F-1 event spectators.

Method

Participants

We selected via a systematic random sampling procedure, spectators (N = 632) who were attending F-1 races held in Shanghai, China, and asked them to respond to a survey. Trained research assistants systematically intercepted every 10th spectator entering the F-1 venue. This data collection procedure continued for three consecutive days. Of the 632 copies of the questionnaire distributed, 60 were discarded owing to having excessive missing values. To deal with the missing values, we employed the listwise deletion method because this method is the most direct approach for treating missing values, and is useful when, as in our study, the sample size is large. Subsequently, we checked outliers via the Mahalanobis distance statistic procedure available in the AMOS program (Byrne, 2009). Based on the analysis, no outlier was found. Thus, the listwise deletion and outlier check resulted in 572 surveys to be included in the data analyses. Of the research participants, men accounted for 50.5% and women accounted for 49.5% of the total. Approximately 80% of the respondents were 31 years of age or younger. In terms of household income, about 80% reported an annual income below ¥60,000. A majority of the sample was composed of current college students or those who had achieved a college degree (71%), with 16% indicating that they possessed an advanced degree. In terms of ethnic background, Asian was predominant in the sample (92.3%).

Measure

We formulated a survey form that contained three sections to assess spectator social motivation, sport consumption behavior, and background information. To measure spectator social motivation, we adopted the Spectator Motivation Scale (SMS; Pease & Zhang, 2001) and modified it to fit into the F-1 setting. The SMS has 19 items in four dimensions (i.e., entertainment (4 items), achievement seeking (7 items), catharsis (4 items), and salubrious effects (4 items). In previous studies, the SMS has been found to have sound psychometric properties (Cronbach’s alpha coefficients ranging from .70–.92; Pease & Zhang, 2001; Zhang et al., 1997). To measure sport consumption behavior, we adapted four items about repurchase intentions from Han and Ryu (2009) and three items about word-of-mouth referrals from those used in previous studies conducted by Zeithaml et al. (1996) and Lee, Lee, and Lee (2005). Demographic information variables were included solely for sample description purposes. In addition, we measured past attendance and used this as a control variable, because researchers have argued that past attendance has proven to be a predictor of future sport consumption (e.g., Funk et al., 2009; Zhang, Pease, Hui, & Michaud, 1995). For testing of content validity we submitted the survey form to a panel of five experts composed of two university professors in sport marketing and three sport managers whose professional experiences were extensively related to F-1 event management. Each panel member was asked to examine the overall content of the items under each of the factors, in terms of their relevance, clarity, and representativeness. Following the feedback of the panel members, we made minor changes to improve the content of the survey form.

The survey form we distributed to respondents was written in both the Chinese and English languages. Because the scales we used in this study were originally developed in English, translating the items into Chinese was necessary. To ensure translation accuracy, a backtranslation was conducted by following the guideline suggested by Brislin (1990) as follows: The original scales were first translated into Chinese by a native Chinese individual who was fluent in English. To ensure the accuracy and equivalence of the translation, the translated version was then converted back into English by a scholar who was fluent in both English and Chinese. The backtranslation ensured that there were no discrepancies between the two versions.

Data Analyses

We used SPSS version 20.0 to calculate descriptive statistics and examine normality of variables (i.e., skewness and kurtosis). Following the two-step approach for structural equation modeling (SEM; Anderson & Gerbing, 1988), we conducted a confirmatory factor analysis (CFA) to examine the psychometric properties of the measurement model, and employed SEM to investigate the relationships between the spectator motivation and sport consumption behavior factors. To examine the overall model fit, we used several fit indices, including chi square (χ²), chi square/degrees of freedom (χ²/df), comparative fit index (CFI), root mean square error of approximation (RMSEA), root mean square residual (RMR), and standardized root mean square residual (SRMR). There were a number of reasons that we chose the selected fit indices. The chi-square statistic is a statistical significance test that tends to be sensitive to sample size. According to Kenny and McCoach (2003), when samples are larger than 400 in number, the chi-square statistic is almost always found to be statistically significant, which was the case in the current study (i.e., N = 572). Because of this, it is suggested that alternative model fit indices be evaluated, which may include one or more of the following: χ²/df (<5.0), RMSEA (<.08), SRMR (<.08), RMR (<.08), and CFI (>.90) (Bollen, 1989; Hu & Bentler, 1999).

We employed three tests of reliability: Cronbach’s alpha (α), composite reliability (CR), and average variance extracted (AVE). We adopted the thresholds of .70, .70, and .50 values to determine α, CR, and AVE, respectively (Bagozzi & Yi, 1988; Fornell & Larcker, 1981; Hair, Black, Babin, & Anderson, 2010). We evaluated convergent validity via factor loadings, and tested discriminant validity via Fornell and Larcker’s (1981) method, by which the squared correlation between any two latent constructs is compared with their AVE values. Upon confirmation of the measurement model, while controlling for past attendance, we conducted an SEM to examine the relationships that the motivation factors had with sport consumption behavior factors. We used the same fit index criteria to examine the structural model as we had used with the measurement model.

Results

Descriptive Statistics

One-sample t tests using 3.0 as the parameter value (i.e., the neutral point on a Likert 5-point scale) indicated that all respondents had a positive perception of items related to spectator motivation and sport consumption behavior, (p < .001) except for one item [M = 3.1, SD = 1.14 (t(571) = 1.88, p < .10)] under salubrious effects. The item “I seek the sensational feeling of the race” displayed the highest mean value [M = 4.2, SD = .75 (t(571) = 38.14, p < .001)]. These results indicate that overall spectator motivation variables were highly regarded when making a decision to attend an F-1 event. Also, respondents in this study possessed favorable behavioral intentions toward the F-1 event. As a result of assessing normality, all skewness and kurtosis values were well within the acceptable criteria (i.e., absolute values of skewness and kurtosis less than 3.0, respectively; Kline, 2010). Descriptive statistics for the spectator motivation variables are presented in Table 1.

Confirmatory Factor Analysis

The CFA revealed that the overall measurement model fit the data well (χ² = 535.04, p < .001, χ²/df = 3.67, CFI = .94, RMSEA = .068, 90% CI = .062–.075, RMR = .052, and SRMR = .053). All of the values of Cronbach’s α (from .85 to .92), CR (from .83 to .91), and AVE (from .56 to .61) were well above the recommended cutoff criteria, indicating that the items within the measurement model were reliable. All factor loadings were statistically significant (p < .001) and were greater than the suggested standard of .707 (Anderson & Gerbing, 1988), ranging from .74 to .82 and indicating good convergent validity. None of the squared correlations was greater than any of the AVE values in the measurement model, providing good evidence for discriminant validity. Information about Cronbach’s α, CR, AVE, and standardized factor loadings is presented in Table 2.

Structural Equation Modeling

The overall structural model showed good fit to the data (χ² = 1010.27, p < .001, χ²/df = 3.55, CFI = .92, RMSEA = .067, 90% CI = .062–.071, RMR = .065, and SRMR = .079). Based on the SEM results, the control variable (i.e., past attendance) was not found to be related to either repurchase intentions (β = -.02, p = .61) or word-of-mouth referrals (β = .06, p = .11). After taking into consideration the effects of the controlled variable, the SEM revealed that salubrious effects (β = .17, p < .001) and achievement seeking (β = .45, p < .001) were positively related to repurchase intentions, explaining a total of 23% of the variance in repurchase intentions; thus, Hypothesis 1 was supported. Neither entertainment (β = -.002, p = .98) nor catharsis (β = -.05, p = .48) was found to be related to repurchase intentions. The SEM further revealed that entertainment (β = .22, p < .01), achievement seeking (β = .29, p < .001), and catharsis (β = .15, p < .001) were positively associated with word-of-mouth referrals. The path from salubrious effects to word-of-mouth was not statistically significant (β = -.06, p = .198). The combined three dimensions accounted for 33% of the variance in word-of-mouth referrals about the F-1 event; thus, Hypothesis 2 was supported.

Discussion

Our purpose in this study was to examine the degree to which social motives explain spectators’ consumption behavior at F-1 events. We found that, among our respondents, achievement seeking and salubrious effects were related to repurchase intentions; and achievement seeking, entertainment, and catharsis were motivating factors that were positively related to word-of-mouth referrals. In the analyses, in an effort to examine only the effect of motivating factors on the two consumption behaviors of F-1 races we statistically controlled for the past attendance behavior of respondents; by doing so, the accuracy of estimating the effect of the motive factors was enhanced. Overall, our findings in the current study lend support to the general notion that social motivation is an important predictor of sport consumption behavior (Funk et al., 2012; Pease & Zhang, 2001; Trail et al., 2003); consequently, nurturing spectator motivations would necessarily be a prioritized component in formulating the marketing mix and promotional schemes of F-1 events.

Although we included four motivating factors in this study, we did not find that all four were related to sport consumption. In terms of the past event consumption experience, the figures of 33% of variance explained in word-of-mouth and 23% explained in repurchase intentions were rather substantial; in particular, when considering that spectator motivation was just one of many psychological tendencies (e.g., attitude, emotion, and perception) included in the model (Byon, Cottingham, & Carroll, 2010), the total variance explained cannot be ignored either theoretically or practically. Indeed, the magnitude of the variance explained could have a significant effect on the increase in spectator consumption behavior of F-1 races. For instance, the standardized beta weights for repurchase intentions fell between .17 and .45, suggesting that with a 1-unit increase in any of the two motivating factors (i.e., achievement seeking and salubrious effects), there was approximately a 3-unit increase (i.e., 17+45/2) in reattendance at the F-1 events. Assuming that each of the two factors can be improved by two units on a standardized scale, a total of a 6-event increase in reattendance may be achieved through improving conditions for the spectator motives of achievement seeking and salubrious effects. This analysis indicates that the study findings have the potential to be of practical value. Nonetheless, it should be noted that other variables that could potentially influence consumption should be incorporated into the SEM model in future studies to enhance the predictability of sport consumption behavior. These may include, but are not limited to, event operation quality (Byon, Zhang, & Connaughton, 2010), consumer satisfaction (Yoshida & James, 2010), and constraints (Kim & Trail, 2010).

The theoretical contribution of this study lies in this being the first application of Sloan’s (1989) spectator motivation theory to spectator consumption of F-1 events. Different motive factors were found to impact on intention for reattendance and intention to refer information by word-of-mouth to others with regard to F-1 events. Event managers and marketers of F-1 host cities need to pay particular attention to the factors that we identified as motivating spectators in this study and develop effective promotional procedures to elevate and maintain high levels of spectator motivation so as to generate adequate return-on-investment for hosting an event. As we found that achievement seeking was a major predictor of both criterion factors, we suggest that it would be constructive for F-1 event host cities to highlight high-achieving drivers and/or their teams in their promotions, to promote individualized psychological connections between consumers and drivers, and to focus on the drama involved in racing and the perseverance shown by racers, both of which relate to human life experience and the human needs hierarchy (Maslow, 1954). As the salubrious effect factor that we tested focuses on the value of relaxation, recreation, and escape from daily routine, creating a feeling for the spectators that they are getting away from their usual daily routine during the F-1 racing events would help elevate the desire of spectators to attend the event again. Similarly, a pleasurable and exciting atmosphere of fun during the event, along with watching aggressive driving, would help enhance both the entertainment and catharsis values of the event, making the event attractive to a spectator and making it likely that s/he would make word-of-mouth reference of the event to family, friends, and other connections.

This study has some limitations. Although we controlled for the influence of past attendance, data in this study were collected from only three F-1 races held in the same season. Typically, a host city of an F-1 event is awarded a 7-year consecutive contract. In future studies, a longitudinal design should be used to enhance the external validity of the findings. In fact, a longitudinal design would provide a strong confidence in establishing causal relationships between the motive factors and consumption behavior in F-1 races. Also, our research was limited to an F-1 Grand Prix event that was held in one Asian city (i.e., Shanghai). Although similarities exist in the market environment when cities are selected to host F-1 events, there may be differences among host cities due to cultural heritage and location. In future studies we would encourage researchers to include spectators at different F-1 events and from multiple cities, countries, and even continents. Finally, as suggested by East et al. (2008), in future studies word-of-mouth as a measure of behavioral intentions should be operationalized as two dimensions (i.e., PWOM and NWOM) so that spectators’ referral behavior and how they relate to sport events such as F-1 races can be better understood.

Appendix

Covariance Matrix

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Chong Kim, College of Physical Education, Department of Global Sport Industry, Hanyang University, #301 Olympic Gym, 17 Haengdang-dong, Seoungdong-gu, Seoul, 133 791, Republic of Korea. Email: [email protected]