An examination of constraints and motivators as predictors of sport media consumption substitution intention.

Larkin, Ben ; Fink, Janet S. ; Trail, Galen T. 等

Introduction

In 2006, researchers Pritchard and Funk explained that there has been a movement toward escalating consumption of sport through media. Although sport teams still care deeply about gate receipts and their impact on the bottom line (Gelb, 2013), the shift to media consumption has, in fact, denoted a trend indicating attendance is becoming a less critical aspect in an organization's profitability (Pritchard & Funk, 2006). Fans' preference to watch games/events on television was among the preeminent concerns facing sport industry practitioners as of 2012 (Luker, 2012). This concern manifested itself in the National Football League (NFL) in January 2014 when three of four first-round playoff games were in danger of being blacked out on local television due to sluggish ticket sales (Schwab, 2014). As Schwab (2014) explained, this may simply be the "most stark example that NFL fans aren't too excited to go to games anymore" (para. 5). This topic has been covered extensively in general publications (e.g., Rovell, 2012); however, it has received scant attention in the academic literature.

Pritchard and Funk (2006) looked at the relationship between attending sporting events and consuming through media, finding that for some this was more of a symbiotic relationship, while others actually substitute media consumption for the more traditional attendance behavior. Additionally, focus has begun to shift from motives (e.g., Funk, Mahony, & Ridinger, 2002; Trail & James, 2001; Wann, 1995) to constraints that impede attendance to sport events (e.g., Kim & Trail, 2010; Trail & Kim, 2011; Trail, Robinson, & Kim, 2008). To this point, however, the impact of constraints on attendance substitution through media has not been studied. Further, research has yet to examine motivators for attendance substitution with sport media consumption (hereafter SMC). In fact, although Pritchard and Funk (2006) implied that constraints to attendance may serve to prompt consumption through media, it is unclear whether the shift toward SMC is more a result of the positive reinforcements of SMC substitution (motivators) or constraining variables that serve as aversive stimuli for attending live events, thus prompting substitution with SMC.

In this paper, the use of the term SMC will refer to the consumption of sport events at home through media. By home, we are referring to an individual's permanent and/or temporary residence. For most fans, this would mean their permanent residence. However, other fans may be residing in a temporary residence that for significant periods of time is their home. For example, a college student's apartment or dormitory may only be a temporary residence, but still represent his or her home during the academic year. Similarly, an individual traveling on business may spend several weeks in a hotel room. While this is certainly not his or her permanent residence, it may represent his or her home while away on business. Given that the possession of a cable or satellite subscription enables individuals to consume through multiple devices, this consumption may include not just television, but also computers, smart phones, and tablets. Therefore, sport media consumption could occur from virtually anywhere at any given time. For example, individuals could be watching at a bar, enjoying the social benefits of watching alongside other fans, and also having the ability to follow their fantasy team as well as the conversation on social media on a mobile device. It would seem the variables considered in this study would be similarly applicable in this context; however, it stands to reason that the impact of certain constraints (e.g., location and cost) and motivators (e.g., ease and comfort) would be mitigated in this case compared to the home setting.

Another element to consider is an individual's level of team identification. Long believed to be the most significant factor in a sport fan's consumption motivation (Wann, Melnick, Russell, & Pease, 2001), team identification has been found to strengthen motives to attend events (Kim, Trail, & Magnusen, 2013). However, researchers have not explored how team identification impacts constraints to attendance, or motivators to substitute attendance with SMC. As such, this study applied Skinner's (1957, 1982) theoretical work on natural consequences as a framework, in an effort to determine the effect of both constraints and motivators on SMC substitution intention and whether this was more a result of the pleasant stimuli (motivators) stemming from SMC or the aversive stimuli for attendance (constraints) that may prompt substitution. Finally, we sought to assess how these relationships were affected by team identification.

Literature Review

Natural Consequences

In his reinforcement theory of motivation, Skinner (1953) proposed that behavior is a function of consequences, both negative and positive. Behavior met with positive consequences has the tendency to be repeated, and in contrast, behavior met with negative consequences does not. Skinner's (1953) theory rests on the notion that looking inside a given organism for an explanation of behavior has the propensity to cloud variables outside the organism that are accessible for analysis. As Skinner (1953) suggested, these variables not only lie outside the organism, but in its immediate environment and environmental history, and make it possible to explain behavior. In this sense, the behaviorist approach is viewed as more of an intervention than as an observation. Illustrating this technique is the famous example where a pigeon raises his head above a certain level and is rewarded with food in order to reinforce the behavior (e.g., Skinner, 1953). As Skinner noted, however, "the only defining characteristic of a reinforcing stimulus is that it reinforces" (1953, p. 72). This brings about the concept of intrinsic consequences, which originate in the behavior itself (Skinner, 1957, 1982). These are often referred to as "natural consequences" (e.g., Skinner, 1982, p. 4). A behavior is reinforced when the consequences produced act to strengthen the behavior. A negative reinforcer is the removal of an unpleasant stimulus, whereas a positive reinforcer is the addition of a pleasant stimulus (Catania, 1998).

This theory is similar in nature to Thorndike's (1927) law of effect. In this work, Thorndike stated that "when 'annoyingness' is attached to a frequent connection and 'satisfyingness' to a rare connection, the latter gains and the former loses until the latter becomes the habitual response" (Thorndike, 1927, p. 212). In effect, the behavior that naturally produces a satisfying rather than dissatisfying feeling becomes habitually repeated. Comunidad Los Horcones (1992) explained that natural reinforcement has suffered from a lack of experimental work and the concept has remained quite vague since its inception; however, the more commonly studied law of effect has remained relevant and has been used as a framework for the study of human choice behavior between two or more alternatives up to the present day (e.g., Navakatikyan, Murrell, Bensemann, Davison, & Elliffe, 2013).

Within sport, constraints to attendance at an event can be aversive stimuli for some individuals. For example, through prior experiences, an individual learns that travel to the venue and parking at the venue is a terrible experience (i.e., stuck in traffic for hours, parking is non-existent and/or extremely expensive). These experiences become aversive stimuli that prevent future consumption of the product (the game) at the venue. However, the individual still wants to consume the product, so they must look for another avenue of consumption; in this case, staying at home and watching the game through media may make for a more attractive option. Thus, the individual substitutes media consumption of the product from home for attending the event at the venue, thereby avoiding the aversive stimuli. In addition, pleasant stimuli relative to watching at home may also exist, motivating the individual to stay home. For example, the comfort and safety of the individual's home may motivate them to stay at home and watch the game rather than attend. Thus, it is critical that both the aversive stimuli to attending and the pleasant stimuli from watching at home be discussed in detail to develop specific hypotheses regarding the variables under consideration in this study.

Aversive Stimuli: Constraints to Attendance

In relation to spectator sport, constraints to attendance are understood to represent "factors that impede or inhibit an individual from attending a sporting event" (Kim & Trail, 2010, p. 191). Past researchers have explained that while they do not necessarily act to block participation, constraints do serve to prompt substitution (Pritchard & Funk, 2006). Constraints, then, can be thought of as aversive stimuli to attendance and their presence could prompt substitution by SMC.

Constraints have long been examined in the leisure domain (e.g., Crawford & Godbey, 1987; Crawford, Jackson, & Godbey, 1991). Only recently, however, have they gained traction in a sport consumer context (e.g., Kim & Trail, 2010; Trail & Kim, 2011; Trail et al., 2008). Crawford and Godbey (1987) grouped leisure constraints into three distinct categories: intrapersonal, interpersonal, and structural. These categories cover the psychological inner states and the interpersonal relationships impacting leisure preferences, as well as the factors that intervene between preferences and participation. Believing that some of the sport spectator constraints could be considered both intrapersonal and interpersonal, Kim and Trail (2010) modified the previously held conception of constraints, grouping the factors into just two categories: internal and external. Internal constraints are the psychological cognitions that curtail behavior (Kim & Trail, 2010). These may include the perception that there is no one with whom to attend, and the perception that significant others have no interest in the sport or event. In contrast, external constraints are the social or environmental aspects that deter or even prevent a behavior (Kim & Trail, 2010; Trail & Kim, 2011), including cost, time, location, and parking. Kim and Trail (2010) examined the relationship between motivators and constraints and their impact on attendance behavior. They found Lack of Success and Leisure Alternatives to be the only significant constraints in the prediction of attendance. One possible leisure alternative an individual could substitute for attending an event is the option of sport media consumption (Trail et al., 2008).

Despite the notion that constraints will prompt substitution (Pritchard & Funk, 2006), we posit that not all constraints will necessarily serve to prompt attendance substitution with SMC. For example, an individual lacking knowledge about a particular sport is unlikely to substitute SMC for attendance, but rather, would be more apt to forego sport consumption altogether. Similarly, leisure alternatives are not likely to trigger SMC over attendance. More likely, the option of playing a recreational sport or attending a concert would serve as a constraint not just for attendance, but for SMC as well. Additionally, commitments to friends and family would constrain both attendance and SMC. Considering such logic, it appears six constraint factors from Trail and Kim's (2011) work could prompt substitution with SMC. These include No Interest from Significant Others, No One to Attend with, Location, Lack of Success, Parking, and Cost. In addition, as Trail et al. (2008) suggested, weather may make for a viable consideration.

H1: There will be a significant positive relationship between each of the seven perceived constraints (a) Location, (b) Parking, (c) Cost, (d) No One to Attend with, (e) Weather, (f) Lack of Success, and (g) No Interest from Significant Others and SMC Substitution Intention.

Pleasant Stimuli: Motives for Sport Media Consumption Substitution

Despite the efficacy of constraints in explaining attendance behavior (Kim & Trail, 2010; Trail & Kim, 2011), motives are also a central predictor in sport consumption decisions (Trail, Fink, & Anderson, 2003). They are understood to represent the "energizing force that activates behaviors" (Hawkins, Best, & Coney, 2004 p. 354), and are thus reflective of naturally occurring positive reinforcement, as their presence increases the likelihood of desired behaviors. Therefore, it is important to consider the possible motivating variables that would positively reinforce SMC substitution to better understand why individuals stay home to consume sport events.

To this point, we are not aware of any existing instrument for the measurement of SMC substitution motivation; however, there have been many past efforts in scale development for motivation of general sport consumption (e.g., Funk et al., 2002; Trail & James, 2001; Wann, 1995). These factors serve as motivating variables for attending events, and could also foster SMC. However, choosing SMC rather than attending in person is triggered by a unique set of motivating variables beyond those historically understood to influence sport consumers. An explanation for this concept lies in the uses and gratifications paradigm (e.g., Katz, Blumler, & Gurevitch, 1973). As was explained by Katz et al., media with different attributes are more likely to satisfy different needs. Conversely, those needs that are "psychologically related or conceptually similar" (Katz et al., 1973, p. 515) will be just as well-fulfilled by options with similar attributes. Indeed a live event and a broadcast event share some of the same attributes. However, while needs such as achievement and escape would be fulfilled by both options, the distinct attributes of SMC create a different set of motives for this consumption mode. Funk et al. (2002) provided further support for this notion in explaining that different consumer motives may trigger different sport activities for various consumer segments. On a related note, Pritchard and Funk (2006) highlighted the fact that a great deal of previous research has focused on the factors driving event attendance, while largely ignoring the usage of media. If these arguments are to be accepted, that is, motives for different sport consumer activities and segments differ, and that literature on SMC remains sparse, then it is only logical to explore this burgeoning consumption mode and search for differences in the motives underlying this distinct consumptive activity.

While no scale has been developed specifically for the motivations underlying SMC, a number of researchers have explored areas relevant to this phenomenon. These areas include fantasy sport (Drayer, Shapiro, Dwyer, Morse, & White, 2010; Dwyer & Drayer, 2010), high-definition television (Dupagne, 1999; Kim & Lee, 2003), and the experience of watching sport events at a local cinema (Fairley & Tyler, 2012). Insights gleaned from this literature suggest that SMC serves as something of a support mechanism for fantasy sport users due to the variety of media sources available while watching a sport event (Drayer et al., 2010; Dwyer & Drayer, 2010). Furthermore, technological attributes such as picture clarity, screen size, and sound have been shown to increase the draw to high-definition television consumption (Kim & Lee, 2003), more specifically for sport viewership (Dupagne, 1999). Fairley and Tyler (2012) corroborated this notion in a qualitative study of sport fans consuming sport events at a local cinema. However, in addition to the notion that consumers appreciate the high degree of technological control (e.g., digital video recording and time shifted playback abilities), Fairley and Tyler also found that the draw of consuming at a local cinema was bolstered by factors such as safety, comfort, broadcast enhancement, and the ease of this consumption mode compared to attending in person. These factors (technological attributes, and the comfort, safety, and ease of watching at home) were in line with stimuli identified in past research (e.g., Gantz & Wenner, 1995; Trail et al., 2003b; Weed, 2010), and it seems they would be equally as significant, or perhaps even more so, for consumers deciding to substitute SMC for attendance.

Indeed, it is apparent that many elements that enhance the modern day sport broadcast, in conjunction with factors such as fantasy sport, technological attributes, and the comfort, safety, and ease of watching at home, positively reinforce SMC. Moreover, much like motives for traditional sport consumption (Trail et al., 2003a), it is evident that these factors are all related to fundamental psychological and social needs (e.g., subsistence, protection, and leisure). This, perhaps, underlies the fact that motives have traditionally been very highly correlated in past spectator motivation research (e.g., Trail & James, 2001; Wann, 1995), which led past work to conceptualize a single second order latent variable comprising all motives (e.g., Trail, Anderson, & Fink, 2000, Trail et al., 2003). This is in contrast to constraints, which have traditionally been studied individually in sport spectator research (e.g., Kim & Trail, 2010). Although Kim and Trail (2010) did examine motives individually, the SMC motives in this study have never before been studied. We expect that--due to their strong conceptual relationship--these factors will be very highly correlated and load highly on a single latent motivator variable. Accordingly, a second order latent variable will be used in the current study that comprises the SMC motivators identified in this literature review (i.e., Fantasy Sport, Technological Attributes, Comfort at Home, Safety at Home, Ease, and Enhancement).

H2: There will be a significant positive relationship between Motivators to stay home (pleasant stimuli) and SMC Substitution Intention.

In review, based on Skinner's (1957, 1982) natural consequences framework, the context of SMC can be viewed as a learned behavior, reinforced through a series of natural consequences. With individuals drawing on past experiences, SMC may be positively reinforced or motivated by, for example, its suitability for fantasy sport participation. Thus, SMC may be repeated because consumers find the consequences "pleasing or satisfying" (Skinner, 1953, p. 81). SMC may also increase if constraints to attending live events are perceived as unpleasant stimuli (e.g., parking, cost, etc.) and staying home is viewed as a viable substitution. In short, an application of Skinner's (1957, 1982) natural consequences framework suggests that an individual's consumption experience will be reinforced through the naturally occurring perception of constraints and motivators, and thus may increase future SMC.

[FIGURE 1 OMITTED]

Overcoming Reinforcement: Team Identification

To this point, our discussion of constraints to attendance and motivators for SMC has not taken into account team identification, which has been shown to drastically impact sport fan behavior. Team identification refers to the degree of a fan's psychological connection to a team (Wann et al., 2001). Wann et al. (2001) suggested that more highly identified fans exhibit different behavioral responses, such as a higher propensity to attend games, even going so far as to suggest that identification with a team and/or athlete is the most significant psychological factor influencing attendance. More recently, Kim and Trail (2010) found team identification to be significant in predicting attendance, as it explained 21% of the variance. Further, they suggested that lack of team success was a constraint that could be overcome by high levels of team identification. Since Kim et al. (2013) found team identification to positively moderate the relationship between motives and attendance intention, it follows that team identification may moderate the effect of constraints and motivators on SMC substitution intention. Highly identified fans should be more apt to overcome constraints to attendance and find the motivators for SMC less appealing than attending. See Figure 1 for an illustration of the proposed relationships.

H3: The relationship between (a) Location, (b) Parking, (c) Cost, (d) No One to Attend with, (e) Weather, (f) Lack of Success, and (g) No Interest from Significant Others and SMC Substitution Intention will be significantly reduced for those high on Team Identification.

H4: The relationship between Motivators and SMC Substitution Intention will be significantly reduced for those high on Team Identification.

Methods

The study consisted of two phases. Both phases received IRB approval. First, the measurement model was tested on a sample of undergraduate students at a large Northeastern university. Second, the measurement and structural models were assessed through a nationwide sample of NFL fans and spectators.

Pilot Study

The goal of the pilot study was to assess the measurement model. A sample of 201 undergraduate students was recruited to assess the internal consistency of the factors and the fit of the measurement model. (1) The questionnaire consisted of constraints, motivators, team identification, and behavioral measures. Items were measured using a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). Constraints consisted of seven subscales adapted from previous studies (e.g., Trail & Kim, 2011) with 21 items. These seven subscales displayed strong psychometric properties, with alpha coefficients ranging from .69 to .93 and average variance explained (AVE) values ranging from .49 to .82 in recent research (e.g., Kim & Trail, 2010; Trail & Kim, 2011). Weather, the final constraint, was suggested by Trail et al. (2008) and was developed for use in this study.

Given that, to our knowledge, there is no instrument in the extant research to measure SMC motivators, these were created for use in this study based on insights gleaned from past sport consumption research (e.g., Drayer et al., 2010; Dupagne, 1999; Dwyer & Drayer, 2010; Fairley & Tyler, 2012; Gantz & Wenner, 1995; Trail et al., 2003b; Weed, 2010), as discussed in our literature review. Prior to data collection, two sport management faculty members with expertise in consumer behavior reviewed these factors for content validity. Modifications were made to some items to reduce ambiguity and improve the clarity of the items. As discussed in our literature review, the motivation dimensions included Fantasy Sport, Technological Attributes, Comfort, Safety, Ease, and Enhancement. Team identification was adapted from the Points of Attachment Index (PAI; Robinson, Trail, & Kwon, 2004). This factor, too, has shown strong psychometric properties, recently exhibiting an alpha coefficient of .96 and construct reliability (AVE) of .88 (Kim et al., 2013). The behavioral measures were adapted from Trail and James (2012). They included single items such as "I am likely to attend future games," and open-ended questions depicting the number of games attended and viewed on television for past and current seasons, as well as intent for future seasons. Finally, the dependent variable representing SMC Substitution Intention included a single item that read, "I am likely to watch future games at home rather than attend." We felt it was particularly important to word this item in such a way as to establish substitution intention. Simply indicating one is likely to watch future games at home does not necessarily capture substitution intentions; however, we believe that indications that one is likely to watch future games at home rather than attend does measure substitution intent.

Results and Discussion

After ensuring that the data was normally distributed, the measurement model was assessed using Mplus 6.0 and showed evidence of reasonable model fit, with [RMSEA = .066, CFI = .898, SRMR = .057, and [chi square]/df ratio = (1375.055/728=1.89)]. However, two factors (i.e., No Interest from Significant Others and No One to Attend with) were very highly correlated. Further, No Interest from Significant Others had poor internal consistency. We believe the poor internal consistency was a result of the construct covering too wide of a domain. Specifically, it covers no interest from family, spouse, and friends. It is plausible that an individual could be deterred from attending an event because his/her family is not interested in attending, but not because of lack of interest from his/her friends. Furthermore, with regard to the high correlation between No Interest from Significant Others and No One to Attend with, we feel that the notion of one's significant others not having interest in attending is conceptually similar and is perhaps just a more specific manifestation of having no one with whom to attend. As such, No Interest from Significant Others was dropped from the analysis. In addition, although Location held together reasonably well, one item reading "the accessibility of the sport venue is difficult" was deemed conceptually different from the other two items, which spoke to the poor neighborhood surrounding the stadium/arena. Accordingly, Location was run as a two-item factor and the model was reassessed. The goodness of fit indices for the CFA on the measurement model indicated good model fit, with [RMSEA = .055, CFI = .938, SRMR = .049, and [chi square]/df ratio = (941.120/587=1.60)]. Moreover, the factors had strong psychometric properties with AVE values ranging from .56 to .88, indicating good construct reliability, and alpha coefficients ranging from .746 to .955, indicating good internal consistency. In short, the preliminary assessment of the measurement model indicated it was appropriate to proceed and test both the measurement and structural model on a more representative sample of sport consumers.

Main Study

The goal of the main study was to again test the measurement model as well as the structural model and hypotheses. Participants were solicited on sport blogs, message boards, and social media groups. Past research has found collegiate sport message board subscribers to attend more games than non-subscribers (Clavio, 2008). Furthermore, it was suggested they were bigger fans than their non-subscribing counterparts, who were characterized more as casual fans. We felt these characteristics would hold for those who were active on both general and NFL team specific sport blogs and message boards. At the very least, we felt this sample would have a history of attendance and SMC experiences on which to draw when assessing the items. A short invitation accompanied by a link to a duplicate of the survey used in the pilot study was posted on message boards as well as on social media pages and the comment section of blogs on popular sport fan sites (e.g., ESPN, Yahoo! Sports, SBNation, Facebook, Twitter, etc.). The short invitation generally read "Hello sport fans, I am conducting some research on sport viewing for my academic work. If you wouldn't mind doing me a huge favor by sparing a few minutes to complete this short survey, it'd be a tremendous help. Thanks so much," but was modified slightly to accommodate social media platforms with character limits (e.g., Twitter). This was posted in approximately 80-100 locations, including general sport blogs and social media groups, as well as team-specific blogs and groups. Data collection was carried out during the NFL season, between the months of November and December of 2013. Informed consent was built in to the beginning of the survey, such that participants had to read the consent form and indicate agreement to participate prior to beginning the survey.

A total of 244 participants were obtained for this phase of the investigation, which is sufficient for structural equation modeling (Kline, 2005). The survey was set to operate with forced completion. Therefore, all completed surveys contained no missing data; however, a total of 78 individuals began, but failed to complete, the survey. These 78 surveys were discarded, leaving us with a completion rate of 76%. The survey asked participants to assess the items in relation to their local NFL team, which may or may not be the same as their favorite team. Given that past research has characterized sport message board subscribers as big sport fans (Clavio, 2008), we felt this step was necessary to ensure that we got a range of team identification levels sufficient for a multigroup moderation analysis. Had participants assessed items in relation to their favorite NFL team, we felt all team identification responses would have been high. The NFL was chosen as the context for the study because we believed constraints such as cost and weather, as well as motivators such as fantasy sport, were most impactful in this context. The NFL is notorious for high price points, and given the time of year the sport is played and the location of some outdoor stadiums, it can also come with potentially harsh weather conditions (Schwab, 2014). Moreover, according to the Fantasy Sport Trade Association, fantasy football is the world's preferred fantasy sport (Fantasy Sport Trade Association, 2014). All NFL markets were represented at least once with the exception of Tennessee, Indianapolis, and Denver. The median for number of times represented was four.

Results

Sample Characteristics

The sample was 77% male with an average age of 31.26 years old. A total of 16.9% of participants had a household income below $19,999 in 2012, while 22.7% made between $20,000 and $49,999, 23.1% made between $50,000 and $79,999, 13.8% made between $80,000 and $109,999, and 9.3% made between $110,000 and $139,999. Just 14.2% of participants made $140,000 or more in 2012. Only 15.5% of participants indicated they had not attended a game in either the previous or current NFL season.

Measurement Model

The means and standard deviations of the variables in this study can be found in Table 1. The study employed the two-step modeling approach, using maximum likelihood estimation to first assess the fit of the measurement model before proceeding to assess the structural model. Subsequently, a multi-group CFA was conducted before assessing whether the hypothesized paths differed across groups, as was hypothesized in H3 and H4. Prior to the assessment of the measurement model, normality of distribution was assessed through an examination of the skewness and kurtosis of the data. All values were within the [+ or -]2.58 range for skewness and [+ or -]2.56 range for kurtosis recommended by Hair, Black, Babin, and Anderson (2009) and thus could be considered normally distributed.

The measurement model was assessed initially and exhibited good model fit, with [RMSEA = .057, CFI = .948, and [chi square]/df ratio = (1058.456/586=1.81)]. Widely accepted guidelines state that the [chi square]/df ratio should fall between two and three (Bollen, 1989), while a CFI of at least .90 and a RMSEA between .06 and .08 are considered adequate (Hu & Bentler, 1999). Therefore, the measurement model fit quite well. Factor loadings of the subscales ranged from .639 to .984, while AVE's ranged from .69 to .91, indicating strong construct reliability (see Table 2 for a list of factor loadings, AVE, and Cronbach's alphas). Discriminant validity was established for all first order factors except for the motive of Ease. The AVE of the Ease subscale failed to exceed the squared correlations between Ease and Comfort and between Ease and Technological Attributes, thus failing the discriminant validity test as per Fornell and Larcker (1981) (see Table 3 for the correlations between latent variables). However, given that we conceptualized the motivators to load on a single second order latent variable, the lack of discriminant validity between these individual motivator variables did not pose a problem in assessing the structural model. Despite the lack of discriminant validity, all items were retained and motivators were run as a second order variable. In brief, the assessment of the measurement model indicated it was appropriate to assess the structural model and evaluate hypotheses.

Structural Model

Accordingly, the first order latent variables representing motivators were loaded onto a second order Motivator latent variable, while the first order variables representing constraints were left to run individually in testing the structural model. Per widely accepted SEM guidelines (e.g., Bollen, 1989; Hu & Bentler, 1999), the structural model exhibited adequate model fit [RMSEA = .063 CFI = .932, [chi square]/df ratio = (1112.744/561 = 1.98)]. Furthermore, the factor loadings of the first order latent motives on the Motivator factor ranged from .639 to .983.

Evaluating Hypotheses 1 & 2

In H1 we proposed that all constraints would have a significant positive relationship with SMC Substitution Intention. There was no significant relationship between Location ([beta] = -.070, p = .485), Parking ([beta] = .021, p = .828), No One to Attend with ([beta] = -.057, p = .429), Weather ([beta] = .028, p = .720), and Lack of Success ([beta] = -.059, p = .416) and SMC Substitution Intention. Therefore, H1a, b, d, e, and f were not supported. The relationship between Cost and SMC Substitution Intention ([beta] = .161, p = .023) was positive and significant, with 2.6% variance explained. Thus, H1c was supported. In H2 we proposed that Motivators would have a significant positive relationship with SMC Substitution Intention. There was a significant positive relationship between Motivators and SMC Substitution Intention ([beta] = .240, p = .003), with 5.8% variance explained. Thus, H2 was supported.

Evaluating Hypotheses 3 & 4

Finally, in H3 and H4 we proposed that Team Identification would moderate the relationship between both the constraints and Motivators and SMC Substitution Intention, such that the effect would be reduced for those with higher levels of Team Identification. To test this moderating effect, a multi-group SEM was conducted where Team Identification levels were split into two groups to ensure significant differences in identification levels; participants who rated their Team Identification at the mid-point and below were placed in the low group (N = 98; M = 2.15) and participants who assessed Team Identification above the mid-point were placed in the high group (N = 146; M = 6.35). Results from a t-test indicated that the means of these two groups were significantly different (t = -34.063, p < .001).

The multi-group CFA fit reasonably well [RMSEA = .076, CFI = .905, and [chi square]/df ratio = (1889.690/1101 = 1.72)], indicating the measurement model did not differ significantly across Team Identification groups. Furthermore, the multi-group structural model showed that constraining the paths to be equal across groups did not make the model fit significantly worse [RMSEA = .076 CFI = .903, and [chi square]/df ratio = (1970.374/1157 = 1.70)], indicating there is no moderating effect of Team Identification on the paths between the independent variables and SMC Substitution Intention. The effects of Location,

Parking, No One to Attend with, Weather, and Lack of Success on SMC Substitution Intention for both low and high Team Identification were not significant and were not different across groups, indicating no moderating effect of Team Identification on the relationship between these factors and SMC Substitution Intention. Therefore, H3a, b, d, e, and f were not supported. The relationship between Cost and SMC Substitution Intention for low Team identification was not significant ([beta] = -.016, p = .904); however, the relationship between Cost and SMC Substitution Intention for high Team Identification was significant ([beta] = .235, p = .005). Although one path coefficient was significant and the other was not, the two paths exhibited overlapping confidence intervals, indicating that they were not significantly different from each other. Therefore, H3c was not supported.

The paths between Motivators (positive reinforcement) and SMC Substitution Intention for both low ([beta] = .290, p = .037) and high ([beta] = .227, p = .023) Team Identification were positive and significant, but not significantly different across groups. Therefore, H4 was not supported (see Table 4 for a list of all tested relationships).

Post-hoc Tests

Following our hypothesis testing, we conducted follow-up analyses on the behavioral items collected as part of the study. On average, participants indicated they watched 7.76 games on television during the previous season, 11.39 games on television during the current season, and intended to watch 10.42 games on television during the following season. The overall mean score for substitution intention was 6.04, but was higher for the high team identification group (M = 6.21) than the low team identification group (M = 5.79). Per the results of a one-way ANOVA, we found this difference in substitution intention to be significant (F = 4.79, p = .03). Even when controlling for the importance of the cost constraint, participants in the high identification group maintained significantly higher substitution intentions (F = 4.99, p = .026). However, when controlling for income, the difference between team identification groups was not significant (F = 2.01, p = .158). We also collected information on participants' attendance behavior and intentions. The overall mean score for attendance intention was 4.63; however, this was significantly higher (F = 72.394, p < .001) for those in the high identification group (M = 5.47) than the low identification group (M = 3.38). As for actual attendance behavior, participants indicated they had attended an average of just under one game in the previous NFL season (M = 0.91), and more than a game on average during the current season (M = 1.42). Participants also intended to attend more than a game per season on average in the following season (M = 1.16). There were, however, no significant differences in actual attendance behavior between identification groups for the previous NFL season (F = 2.23, p = .137) or the current NFL season (F = 1.32, p = .253), but consistent with responses on the attendance intention scale, highly identified participants indicated they intended to attend a significantly higher number of games in the following season (M = 1.37, F = 3.992, p = .047) than participants in the low identification group (M = 0.87).

General Discussion

The current research provides initial evidence of the effect of both constraints to attendance and SMC motivators on SMC substitution intention, thereby substantiating the role natural consequences play in this context. In total, the model explains approximately 10% of the variance in SMC Substitution Intention. Nevertheless, the only meaningful constraint in explaining consumers' SMC Substitution Intentions was Cost. The significance of Cost stood in contrast to the work of Kim and Trail (2010); the finding is understandable given the current study was conducted in the context of individuals' local NFL teams, a sport notorious for high price points (Schwab, 2014). Kim and Trail's (2010) work, on the other hand, utilized Women's National Basketball Association (WNBA) consumers, a sport that is more affordably priced. Moreover, it seems cost is a more insurmountable constraint than others. For example, a fan may be willing to overcome frigid weather, a bad neighborhood, and the difficulty of parking to attend an event; however, if he/she simply does not possess the financial means to attend the event, there is little they can due to negotiate and/or rectify such a constraint. Further support for this notion can be gleaned from our finding that highly identified fans exhibited significantly higher SMC substitution intentions than those who were low on identification, but the difference was not significant in the presence of income.

The finding that constraints (aversive stimuli for attending) were, for the most part, insignificant in predicting SMC Substitution Intention is consistent with the results from previous research (Kim & Trail, 2010). From a theoretical standpoint, there is a precedent of pleasant stimuli as a stronger predictor of behavior than aversive stimuli given that, in past work, the only two constraints to explain any significant amount of variance in attendance were Lack of Success (10%) and Leisure Alternatives (3%; Kim & Trail, 2010). In that particular study, the survey was distributed at the venue of a team in second to last place in their conference. As such, it would be expected that an unsuccessful team would serve to constrain attendance for many spectators.

The current study, on the other hand, was conducted using a nationwide sample of NFL fans and spectators, many of whom lived in markets with successful teams. In addition, given that SMC is a leisure alternative in and of itself, Leisure Alternatives was not identified as a constraint that would serve to prompt an individual to substitute SMC for sport event attendance. In short, the only two constraints of any significance in Kim and Trail's (2010) study were either not as pertinent or not applicable in this study and could be part of the reason for the lack of significance of Constraints in this study. Nevertheless, the results of this study should give NFL officials--particularly those in cold weather climates--some peace of mind, as it seems that fans are not substituting with SMC at home due to constraints such as weather, location, etc.

Motivators (positive natural reinforcement) had a statistically significant effect on SMC Substitution Intention. The Motivators factor encompassed the dimensions of Fantasy Sport, Technological Attributes, Comfort, Safety, Ease, and Enhancement, factors that do not necessarily constrain attendance, but were posited to reinforce SMC. The path loadings of the second order Motivator latent variable suggest this factor was driven by Technological Attributes, Comfort, and Ease. Therefore, consistent with past research (Dupagne, 1999; Fairley & Tyler, 2012; Gantz & Wenner, 1995; Trail et al., 2003b), it seems individuals exhibit SMC substitution intentions in part due to the ability to watch a crystal clear broadcast of the event from the comfort and relaxation of their own home. Four of the seven SMC Motivators (e.g., Technological Attributes, Comfort, Ease, and Enhancement) were at least marginally important (mean greater than 4 on a 1 to 7 scale) to participants, whereas just one Constraint (e.g., Cost) was assessed in this way. These results suggest that, with the exception of cost, constraints do not necessarily prompt individuals to substitute SMC for attendance. Rather, it seems perhaps some consumers simply prefer the home experience due, at least in small part, to these positive reinforcers.

We expected Team Identification to moderate the relationship between constraints and Motivators and SMC Substitution Intention, such that more highly identified individuals would be able to overcome these factors and the strength of the relationships would be reduced. In contrast to past work (e.g., Kim et al., 2013), however, team identification did not serve to alter the strength of these relationships in the SMC context. Furthermore, highly identified fans in the current study exhibited significantly higher substitution intentions that held even when controlling for the importance of cost. Since individuals in the high team identification group in this study exhibited significantly greater and more frequent SMC behaviors than attendance behaviors, the findings lend some credence to Pritchard and Funk's (2006) suggestion that media-dominant consumers are perhaps as highly involved as those placing an emphasis on attendance. At the root of this finding could be the fact that the home viewing experience has improved at a rate far greater than the stadium experience over the last several decades. For example, following from social identity theory, Smith and Smith (2012) indicated that highly identified fans can now publicly display their association with their favorite team by way of social media, an ability previously reserved for the live event. Their content analysis of tweets sent out during the 2012 College Baseball World Series suggested that this was a medium used for commentary, cheering, encouragement, celebration, and jeers. Interestingly, these behaviors practiced on social media are akin to BIRGing (basking in reflected glory) and derogating the out-group, practices long associated with the behavior of highly identified fans (Branscombe & Wann, 1992). In addition, commentators have argued that fans' interest in a sport and/or league as a whole, rather than just one team, has increased rapidly in recent years (Duffey, 2013). Since fans are better able to satisfy these multiple interests while watching at home, foregoing the live event may be an attractive option. In short, it seems the home viewing experience has changed quite substantially in the last 15-20 years in ways particularly well-suited for highly identified fans of sport. This has created an environment that seems particularly attractive for this population, though substantial research is needed to assess the validity of these proposed explanations.

Implications, Limitations, and Future Research

To our knowledge, the current research is the first to examine the factors underlying the phenomenon of intentions to substitute SMC for attendance, a consumption mode proliferating in appeal in recent years (Pritchard & Funk, 2006). In so doing, the study provides a foundation and starting point for future research to further examine constraints and motivators for SMC intention and also provides theoretical contributions to the field. First, by exhibiting the significance of Motivators (positive natural reinforcement) and Cost on SMC Substitution Intention, it extends Skinner's (1957, 1982) natural consequences framework to a sport consumption context. That is, although aversive stimuli for attendance had only limited effectiveness in predicting SMC Substitution Intentions, SMC Motivators were shown to represent positive natural reinforcements that were significant even for those highly identified with the team. Thus, the current study shows that, while literature has suggested constraints serve to prompt substitution (Pritchard & Funk, 2006), the phenomenon of consumers substituting SMC for attendance might be more a result of Motivators, or the pleasant stimuli of home. In addition, the study extends several research streams in the spectator sport literature. First, it extends the work done by Trail and his colleagues (e.g., Kim & Trail, 2010; Trail & Kim, 2010; Trail et al., 2008) by measuring the effect of constraints to sport event attendance in the context of consumers' intentions to substitute with SMC. Second, it extends the team identification literature by examining whether one's psychological connection to a team can influence their SMC intention. The finding that the impact of constraints and motivators did not differ between highly and lowly identified fans, and those in the high team identification group actually exhibited slightly stronger SMC substitution intentions, represents a potential contribution to the literature. It suggests the consumption habits of fans may be changing slightly, a finding with implications for future research, though further investigation and/or replication is needed. Finally, the study answers Pritchard and Funk's (2006) call for future research to examine the relationship between media use and game attendance, specifically exploring how motivation and constraints alter consumption.

In addition to these theoretical and empirical contributions, the study also makes several contributions to practice. With NFL teams recently struggling to avoid local TV blackouts due to sluggish ticket sales for the first round of the playoffs (Schwab, 2014), it is no surprise that fans preferring to watch games at home was identified as one of the most prominent concerns among sport industry practitioners in a recent industry survey (Luker, 2012). The current study affords sport practitioners some potential reasons why fans may seem to prefer to stay home. As this study shows, it may be more a product of the appealing elements of the home experience including comfort, enhancement, and technological attributes, than factors such as location, parking, and weather that serve to constrain attendance. While cost was statistically significant, no other constraints appear to play a significant role in prompting individuals to substitute SMC for attendance.

While the current study offers some initial evidence, sport marketers should replicate the study on their own consumers to determine the extent to which the above aspects impact attendance, as many factors could vary by location. If there are negative impacts, then sport marketers would know which aspects to address to reduce potential constraints and improve the venue experience to improve attendance and possibly avoid future local TV blackouts. For example, organizations could enhance the live experience by improving facility comfort, technological capabilities, and making it as easy as possible for fans upon arriving at the venue. This could include amenities such as cushioned seats, in-seat concession ordering, and wider concourses and stairwells to make entry and exit as easy as possible. As Ourand (2015) noted, organizations are starting to leverage technology to enhance the live experience, as the Washington Capitals of the National Hockey League recently introduced an in-arena fantasy game for spectators. In addition to increasing fan engagement and the enjoyment of the experience, this also allows the organization to acquire valuable information about their fans. Furthermore, teams could provide fans at the stadium with an experience fans at home cannot access. This may include exclusive looks into the locker room during halftime as well as audio of players and coaches wearing microphones during games that are available only to those with a game ticket.

On the other hand, sport organizations may want to embrace the fact that there is going to be a large contingent of fans watching from home and leverage technology to increase engagement for this segment. For example, teams may want to take advantage of the dual-screening trend (i.e., fans following the conversation on social media and managing their fantasy teams during the game). By participating in the discussion--both about the game as well as its implications for fantasy sport--teams could increase the engagement level and enjoyment of the home experience.

Despite these theoretical and practical contributions, this research does come with some limitations. First, data was collected in relation to just one sport and league (i.e., NFL). It is possible that consumption of other sports may differ from professional football. With respect to the sample, two more limitations must be acknowledged. Although efforts were made to obtain a nationwide representative sample, a convenience sample and not a true random sample was obtained. While all markets in the NFL, save three, were represented in the study, it remains possible that different results could emerge with a true random sample. Finally, females were underrepresented in the main study, which is noteworthy given that females have demonstrated significant differences in consumer motivations in past work (e.g., Fink & Parker, 2009).

As for future research, given the lack of significance of most of the constraints in this study, future studies could attempt to identify constraints that may be more impactful on individuals' SMC substitution intentions. Second, future work should examine additional variables beyond team identification that may serve to moderate and/or mediate the relationships between constraints and motivators and SMC substitution intentions, thereby explaining a higher amount of variance. Third, future studies could take a cross-sectional look at males and females and search for differences in their SMC intentions. Finally, given that highly identified fans exhibited no significant differences in SMC intentions and a significantly greater substitution intention, future research should investigate why even highly identified sport fans--a segment long shown to exhibit a higher preference to attend--may now be just as content to watch sport events at home.

References

Bollen, K. A. (1989). Structural equation with latent variables. New York: John Wiley & Sons.

Branscombe, N. R. & Wann, D. (1992). Role of identification with a group, arousal, categorization processes, and self-esteem in sport spectator aggression. Human Relations, 45, 1013-1033.

Catania, A. C. (1998). Learning (4th ed.). Upper Saddle River, NJ: Prentice Hall.

Clavio, G. (2008). Demographics and usage profiles of users of college sport message boards. International Journal of Sport Communication, 1, 434-443.

Comunidad Los Horcones. (1992). Natural reinforcement: A way to improve education. Journal of Applied Behavior Analysis, 25, 71-75.

Crawford, D. W., & Godbey, G. (1987). Reconceptualizing barriers to family leisure. Leisure Sciences, 9, 119-127.

Crawford, D. W., Jackson, E. L., & Godbey, G. (1991). A hierarchical model of leisure constraints. Leisure Sciences, 13, 309-320.

Drayer, J., Shapiro, S. L., Dwyer, B., Morse, A. L., & White, J. (2010). The effects of fantasy football participation on NFL consumption: A qualitative analysis. Sport Management Review, 13, 129-141.

Duffey, K. (2013, October 1). The real reason college football live game attendance is down. Saturday down South. Retrieved from http://www.saturdaydownsouth.com/2013/college-football-live-game-attendance/

Dupagne, M. (1999). Exploring the characteristics of potential high-definition television adopters. Journal of Media Economics, 12, 35-50.

Dwyer, B., & Drayer, J. (2010). Fantasy sport consumer segmentation: An investigation into the differing consumption modes of fantasy football participants. Sport Marketing Quarterly, 19, 207-216.

Fairley, S., & Tyler, B. D. (2012). Bringing baseball to the big screen: Building sense of community outside of the ballpark. Journal of Sport Management, 26, 258-270.

Fantasy Sport Trade Association. (2014). Industry demographics. Retrieved from http://www.fsta.org/?page=Demographics

Fink, J. S., & Parker, H. M. (2009). Spectator motives: Why do we watch when our favorite team is not playing? Sport Marketing Quarterly, 18, 210-217.

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

Funk, D. C., Mahony, D. F., & Ridinger, L. L. (2002). Characterizing consumer motivation as individual difference factors: Augmenting the sport interest inventory (SII) to explain level of sport. Sport Marketing Quarterly, 11, 33-43.

Gantz, W., & Wenner, L. A. (1995). Fanship and the television sports viewing experience. Sociology of Sport Journal, 12, 56-74.

Gelb, M. (2013, April 26). Why Phillies attendance numbers matter. Philly.com. Retrieved from http://www.philly.com/philly/blogs/phillies_zone/Why-Phillies-attendance-numbers-matter.html

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Hawkins, D. I., Best, R. J., & Coney, K. A. (2004). Consumer behavior: Building marketing strategy. Boston, MA: McGraw-Hill Irwin.

Hu, L., & Bentler, P. (1999). Fit indices in covariance structure modeling: Sensitivity under parameterized model misspecification. Psychological Methods, 3, 424-453.

Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public Opinion Quarterly, 37, 509-523.

Kim, H. S., & Lee, S. T. (2003). Exploring the characteristics of DVD home theater system adopters. Mass Communication & Society, 6, 267-290.

Kim, Y. K., & Trail, G. (2010). Constraints and motivators: A new model to explain sport consumer behavior. Journal of Sport Management, 24, 190-210.

Kim, Y. K., Trail, G. T., & Magnusen, M. J. (2013). Transition from motivation to behaviour: Examining the moderating role of identification (ID) on the relationship between motives and attendance. International Journal of Sports Marketing & Sponsorship, 14, 190-211.

Kline, R. B. (2005). Principles and practice of structural equation modeling (3rd ed.). New York, NY: The Guilford Press.

Luker, R. (2012, October 1). Shifting interest by age, gender gives MMA a fighting chance. Street & Smith's SportsBusiness Journal, 15, 17.

Navakatikyan, M. A., Murrell, P., Bensemann, J., Davison, M., & Elliffe, D. (2013). Law of effect models and choice between many alternatives. Journal of Experimental Analysis of Behavior, 100, 222-256.

Ourand, J. (2015, March 2). In arena fantasy game? That's a capital idea. Street & Smith's SportsBusiness Journal. Retrieved from http://www.sportsbusinessdaily.com/Journal/Issues/2015/03/02/Media/Sports-Media.aspx

Pritchard, M. P., & Funk, D. C. (2006). Symbiosis and substitution in spectator sport. Journal of Sport Management, 20, 299-321.

Robinson, M. J., Trail, G. T., & Kwon, H. (2004). Motives and points of attachment of professional golf spectators. Sport Management Review, 7, 167-192.

Rovell, D. (2012, November 16). Best seat in the house. ESPN Outside the Lines. Retrieved from http://espn.go.com/espn/otl/story/_/id/8636927/nfl-taking-note-many-fans-watching-games-tv-beats-going-stadiums

Schwab, F. (2014, January 1). NFL should be alarmed that three of four playoff games, including Green Bay's home game, still not sold out. Yahoo! Sports. Retrieved from http://sports.yahoo.com/blogs/nfl-shutdown-corner/nfl-alarmed-three-four-playoff-games- including-green-213137570-nfl.html

Skinner, B. F. (1953). Science and human behavior. Toronto, Ontario: The MacMillan Company.

Skinner, B. F. (1957). Verbal behavior. New York, NY: Appleton-Century-Crofts.

Skinner, B. F. (1982). Contrived reinforcement. The Behavior Analyst, 5, 3-8.

Smith, R. L., & Smith, K. D. (2012). Identity in Twitter's hashtag culture: A sport-media-consumption culture case study. International Journal of Sport Communication, 5, 539-557.

Thorndike, E. L. (1927). The law of effect. The American Journal of Psychology, 39, 212-222.

Trail, G. T., Anderson, D. F., & Fink, J. S. (2000). A theoretical model of sport spectator consumption behavior. International Journal of Sport Management, 1, 154-180.

Trail, G. T., Fink, J. S., & Anderson, D. F. (2003a). Sport spectator consumption behavior. Sport Marketing Quarterly, 12, 8-17.

Trail, G. T., & James, J. D. (2001). The motivation scale for sport consumption: Assessment of the scale's psychometric properties. Journal of Sport Behavior, 24, 108-127.

Trail, G. T., & James, J. D. (2012). Sport consumer behavior. Seattle, WA: Sport Consumer Research Consultants LLC.

Trail, G. T., & Kim, Y. K. (2011). Factors influencing spectator sport consumption: NCAA women's basketball. International Journal of Sports Marketing & Sponsorship, 13, 60-82.

Trail, G. T., Robinson, M. J., Dick, R. J., & Gillentine, A. J. (2003b). Motives and points of attachment: Fans versus spectators in intercollegiate athletics. Sport Marketing Quarterly, 12, 217-227.

Trail, G. T., Robinson, M. J., & Kim, Y. K. (2008). Sport consumer behavior: A test for group differences on structural constraints. Sport Marketing Quarterly, 17, 190-200.

Wann, D. L. (1995). Preliminary validation of the sport fan motivation scale. Journal of Sport & Social Issues, 19, 377-396.

Wann, D. L., Melnick, M. J., Russell, G. W., & Pease, D. G. (2001). Sport fans: The psychology and social impact of spectators. New York, NY: Routledge.

Weed, M. (2010). Sport fans and travel--Is 'being there' always important? Journal of Sport & Tourism, 15, 103-109.

Endnote

(1) Participants in the pilot study consisted of sport management undergraduate students at a large northeastern university who were asked to complete a paper and pencil survey. This survey was completed during class time. There were no surveys that included more than a few items of missing data. Accordingly, any missing items were replaced with the mean score.

Ben Larkin, MS, is a doctoral candidate in the Department of Sport Management at the University of Massachusetts Amherst. His research interests include marketing, consumer behavior, sport media consumption, and consumer emotion.

Janet S. Fink, PhD, is a professor of sport management and chair of the Mark H. McCormack Department of Sport Management at the University of Massachusetts Amherst. Her research interests include sport consumer behavior, particularly the marketing of female athletes and women's sport.

Galen T. Trail, PhD, is a professor and director of the Master's of Sport Administration and Leadership program at Seattle University. His research focuses on sport consumer behavior.

Table 1
Means and Standard Deviations

Factors                               Mean    SD

Constraints                           3.25
No Interest from Significant Others   3.51   1.81
Location                              2.41   1.57
Parking                               2.87   1.86
Cost                                  5.44   1.80
No One to Attend with                 2.84   1.84
Weather                               2.99   1.86
Lack of Success                       2.72   1.82
Motivators                            4.13
Fantasy                               3.46   1.97
Technological Attributes              4.28   2.01
Comfort                               4.73   1.96
Safety                                3.62   2.00
Ease                                  4.66   1.94
Enhancement                           4.03   1.90
Team Identification                   4.67   2.34
SMC Substitution Intention            6.04   1.50

Table 2
Second Order Factor Loadings ([beta]), First Order Factor
Loadings ([lambda]), Alpha Coefficients ([alpha]), and
Average Variance Extracted

Items                                               [beta]   [lambda]

Team Identification
Being a fan of the team is important to me                   .893
I am a committed fan of the team                             .984
I consider myself to be a "real" fan of the team             .978

Constraints

Lack of Success

The team loses more than they win                            .884
The team is in the bottom half of the                        .965
  conference/league
The team doesn't win many games                              .932

No One to Attend with

There is no one to go to the game with me                    .871
A lack of friends to go to the game with me                  .953
My spouse/significant other won't go to the                  .639
  game with me

Parking

The accessibility of parking for the sport                   .864
  venue is poor
Ease of parking at the arena/stadium is bad                  .891
A lack of close parking to the sport venue                   .872

Cost

The financial cost of going to a game is too high            .869
The cost of attending games is too much                      .920
Tickets cost too much                                        .909

Weather

It is often too hot/cold at the stadium/arena                .863
The weather makes it uncomfortable at the sport              .948
  venue
The weather conditions at the sport venue are                .909
  not ideal

Location

The area/neighborhood surround the arena/stadium             .854
  is not nice
The sport venue is located in a bad part of town             .850

Motivators

Fantasy                                             .639

My home is a better environment for                          .786
  participating in fantasy sport
It is easier to monitor the progress of my                   .894
  fantasy sport team(s) at home
There are a variety of media sources available               .907
  at home which are useful for fantasy sport

Technological Attributes                            .887

Technological aspects (e.g., HDTV, surround                  .827
  sound, DVR, playback, etc.) available when
  watching at home
When watching at home there are more                         .917
  technological attributes (e.g., HDTV, surround
  sound, DVR, playback, etc.) that add to the
  experience
The technological characteristics of the home                .929
  environment (e.g., HDTV, surround sound, DVR,
  playback, etc.) have made staying home more
  enjoyable

Comfort                                             .983

I am more comfortable at home                                .787
The seats are more comfortable at home                       .911
My home is more comfortable than the sport venue             .946

Safety

I feel safer at home                                .765     .845
My home environment provides a sense of security             .941
My home is more comfortable than the sport venue    .949     .940

Ease

It is easier to watch sport events at home                   .768
It requires less effort than attending a game                .823
I don't have to exert myself as I do when going     .735     .754
  to the sport venue

Enhancement

The commentators, statistics, live updates, and              .869
  coverage enhance the consumption experience
The media broadcast enhances the consumption                 .965
  experience
The media broadcast adds to the experience of                .932
  watching a sport event at home

Items                                               [alpha]   AVE

Team Identification                                 .966      0.91
Being a fan of the team is important to me
I am a committed fan of the team
I consider myself to be a "real" fan of the team

Constraints

Lack of Success                                     .948      0.86

The team loses more than they win
The team is in the bottom half of the
  conference/league
The team doesn't win many games

No One to Attend with                               .854      0.69

There is no one to go to the game with me
A lack of friends to go to the game with me
My spouse/significant other won't go to the
  game with me

Parking                                             .907      0.77

The accessibility of parking for the sport
  venue is poor
Ease of parking at the arena/stadium is bad
A lack of close parking to the sport venue

Cost                                                .926      0.81

The financial cost of going to a game is too high
The cost of attending games is too much
Tickets cost too much

Weather                                             .932      0.82

It is often too hot/cold at the stadium/arena
The weather makes it uncomfortable at the sport
  venue
The weather conditions at the sport venue are
  not ideal

Location                                            .838      0.73

The area/neighborhood surround the arena/stadium
  is not nice
The sport venue is located in a bad part of town

Motivators

Fantasy                                             .896      0.75

My home is a better environment for
  participating in fantasy sport
It is easier to monitor the progress of my
  fantasy sport team(s) at home
There are a variety of media sources available
  at home which are useful for fantasy sport

Technological Attributes                            .920      0.80

Technological aspects (e.g., HDTV, surround
  sound, DVR, playback, etc.) available when
  watching at home
When watching at home there are more
  technological attributes (e.g., HDTV, surround
  sound, DVR, playback, etc.) that add to the
  experience
The technological characteristics of the home
  environment (e.g., HDTV, surround sound, DVR,
  playback, etc.) have made staying home more
  enjoyable

Comfort                                             .908      0.78

I am more comfortable at home
The seats are more comfortable at home
My home is more comfortable than the sport venue

Safety

I feel safer at home                                .935      0.83
My home environment provides a sense of security
My home is more comfortable than the sport venue    .821      0.61

Ease

It is easier to watch sport events at home
It requires less effort than attending a game
I don't have to exert myself as I do when going     .942      0.85
  to the sport venue

Enhancement

The commentators, statistics, live updates, and
  coverage enhance the consumption experience
The media broadcast enhances the consumption
  experience
The media broadcast adds to the experience of
  watching a sport event at home

Table 3
Correlations among Latent Variables

                                1        2       3       4

1. Team Identification          1
2. Location                   -0.222     1
3. Parking                    -0.159   0.612     1
4. Cost                       -0.018   0.141   0.322     1
5. No One to Attend with      -0.113   0.385   0.246   0.099
6. Weather                    -0.195   0.262   0.443   0.238
7. Fantasy Sport              -0.147   0.271   0.358   0.168
8. Technological Attributes   -0.122   0.276   0.375   0.338
9. Comfort                    -0.134   0.297   0.430   0.411
10. Safety                    -0.160   0.515   0.527   0.278
11. Ease                      -0.108   0.292   0.388   0.434
12. Enhancement               -0.084   0.380   0.411   0.248
13. Lack of Success           -0.180   0.402   0.225   0.005

                                5       6       7       8

1. Team Identification
2. Location
3. Parking
4. Cost
5. No One to Attend with        1
6. Weather                    0.209     1
7. Fantasy Sport              0.176   0.492     1
8. Technological Attributes   0.207   0.456   0.653     1
9. Comfort                    0.150   0.452   0.612   0.869
10. Safety                    0.211   0.452   0.493   0.613
11. Ease                      0.213   0.468   0.529   0.806
12. Enhancement               0.211   0.425   0.513   0.763
13. Lack of Success           0.346   0.140   0.222   0.284

                                9      10      11      12     13

1. Team Identification
2. Location
3. Parking
4. Cost
5. No One to Attend with
6. Weather
7. Fantasy Sport
8. Technological Attributes
9. Comfort                      1
10. Safety                    0.762     1
11. Ease                      0.958   0.731     1
12. Enhancement               0.695   0.546   0.654     1
13. Lack of Success           0.243   0.279   0.214   0.221   1

Table 4
Testing of Hypothesized Relationships on SMC Substitution Intention

                        Low Team ID (N = 98)

Location                [beta] = -.145 (-.603, .304), p = .397
Parking                 [beta] = .105 (-.344, .554), p = .548
Cost                    [beta] = -.016 (-.348, .317), p = .904
No One to Attend with   [beta] = .069 (-.253, .391), p = .582
Weather                 [beta] = .104 (-.244, .452), p = .443
Lack of Success         [beta] = .040 (-.242, .323), p = .712
Motivators              [beta] = .290 (-.068, .648), p = .037

                        High Team ID (N = 146)

Location                [beta] = -.066 (-.375, .245), p = .588
Parking                 [beta] = .023 (-.273, .324), p = .824
Cost                    [beta] = .235 (.021, .449), p = .005
No One to Attend with   [beta] = -.087 (-.308, .134), p = .312
Weather                 [beta] = -.018 (-.260, .224), p = .849
Lack of Success         [beta] = -.142 (-.380, .097), p = .127
Motivators              [beta] = .227 (-.031, .485), p = .023

                        Whole Group (N = 244)

Location                [beta] = -.070 (-.328, .188), p = .485
Parking                 [beta] = .021 (-.231, .274), p = .828
Cost                    [beta] = .161 (-.022, .343), p = .023
No One to Attend with   [beta] = -.057 (-.241, .127), p = .429
Weather                 [beta] = .028 (-.170, .226), p = .720
Lack of Success         [beta] = -.059 (-.245, .127), p = .416
Motivators              [beta] = .240 (.029, .450), p = .003

* 90% Confidence Intervals