Profiling Major Sport Event Visitors: The 2002 Commonwealth Games

Abstract

It has become common practice for governments and municipalities around the world to bid for the right to host a major sporting event. Prior to embarking on the bidding process, politicians attempt to determine whether such an event will be of value to their municipality; and often focus on the estimated economic consequences of hosting such an event. Frequently, studies are commissioned to predict the event's economic value. However, these studies often miscalculate the potential impact of sport event visitors as consumers. We argue that enhanced profiling of these visitors will enable a more accurate assessment of economic impact. The current research surveys 1,196 spectators of the 2002 Commonwealth Games to demonstrate four important aspects of visitor profiles related to economic impact: (i) visitor typology, (ii) sport tourist behaviors, (iii) consumption patterns determined by interest, and (iv) consumption patterns determined by distance traveled. Overall, the work makes three important contributions to the literature by: (i) empirically supporting that different sports attract different market demographics, (ii) underlining the need for segmentation in economic impact studies, and (iii) identifying the need to develop metrics of economic impact analysis that consider segmentation effects.

Keywords:

• Sport Tourism
• Economic Impact
• Major Sport Events
• Commonwealth Games
• Event Management
• Event Visitors
• Spectators

Introduction

Large municipalities are often very interested in attracting major sport events. As a result, the bidding competition for the rights to host such events is quite fierce. Häußermann & Siebel (1993) examined the ‘Festivalisation’ of city politics and described how major sporting events can attract ‘fresh’ money to the host city by providing additional resources to develop and restructure the city and improve its image. However, their study is quite critical about the magnitude of these effects (Häußermann & Siebel, 1993). Indeed, many politicians believe that hosting major events will attract both direct and indirect spending to the local economy. Visitors make expenditures directly related to the event, while indirect benefits include such items as future tourism from destination marketing, post-event economic growth, and governmental subsidies towards the construction of the infrastructure needed to host the event.

Major sport events such as the Olympic Games, Pan American Games, FIFA Football World Cup or Commonwealth Games are large-scale events that require a large number of sport facilities and training sites. These facilities must meet the highest international standards. Further, the International Olympic Committee (IOC) and other International Federations (IF) have recognized that spectator capacity should be reduced in order to avoid the construction of ‘white elephants’ (IOC, 2003). While it is obvious that the potential television audience for major sport events far exceeds the number of visitors to a city, we suggest that visitors to these events should be given special consideration. In fact, such visitors create an atmosphere that differs considerably from local-only events (Preuss & Messing, 2003). They consume a number of products during their stay in the city and may also provide valuable word-of-mouth advertising for a host city as a travel destination.

A well known method of measuring the benefits of events is known as cost-benefit-analysis, where the direct, indirect, and intangible costs and benefits of an event are measured. These cost-benefit assessments need to include several sources of information about the visitors on a number of specific variables. However, it is important to note that ticket revenue and expenditures differ among the visitors. Thus, the considerable contribution by visitors towards the overall economic impact is often miscalculated due to the complexity of measuring it. Many studies investigate the impact of events on tourism (e.g. Getz, 1989, 1991; Hall, 1992; Kang & Perdue, 1994; Dwyer et al., 2004), and only recently have researchers considered the event-related impact on spending in the local economy. For example, Dwyer et al. (2006) suggest that the use of a computable general equilibrium approach (CGE) is better suited to distinguish the effects on the host regions as compared to other regions.

The considerable potential economic benefit of hosting a major sporting event has played a key role in attracting the interest of cities. The influx of a large number of visitors to the host city is partly responsible for driving this economic benefit. When calculating economic impact, one must consider the number of visitors coming to the city, their consumption patterns, the duration of their stay, and the ‘crowded-out’ effects (i.e. the opportunity costs as some long-term tourists will avoid the destination due to the event). Thus, an exact assessment of the tourism-based economic impact is an important component in calculating the overall potential economic benefit to a city staging a major sport event (Kesenne, 2005; Preuss, 2005).

The works of Crompton (1999), Gratton et al. (2000), and Coleman (2004) focus on small and medium-sized special sport events. A major sport event is defined here as ‘irregular major international spectator events generating significant economic activity and media interest’ (Coleman, 2004, p. 11). The 2002 Commonwealth Games can be described as a multi-sport event that stages world-class sport competitions in one city at the same time. This research explores the specifics of the effects of visitors through an assessment of visitors to a major sport event, the 2002 Commonwealth Games in Manchester, UK. Hence, this paper examines three issues:

1. The complexity of measuring the different groups (event segments) to be considered in economic impact studies as shown by visitors to the 2002 Commonwealth Games.

2. The differences between visitors to the 2002 Commonwealth Games and normal tourists (i.e. tourists in Manchester for reasons other than the Games) coming to the city of Manchester on a variety of measures, such as: (i) length of stay; (ii) spending on tickets, merchandise, accommodation, food, and shopping; and (iii) interest in traveling before and/or after the event.

3. Segmenting 2002 Commonwealth Games visitors by their duration of stay, outlay/effort to attend the Games or distance traveled to Manchester.

Overview of Literature

It is believed that sport is the primary reason for travel in approximately 25% of all holidays (Weed & Bull, 1998). The literature has responded to this point and research related to sport tourism and major sport events has increased significantly in recent years. Many studies (e.g. Redmond, 1990; Dreyer, 1998; Weed & Bull, 1997; Standeven & De Knop, 1999; Kartakoullis et al., 2003; Preuss, 2004) have highlighted the correlation between sport and tourism. This paper's focus is on sport event tourism, or visiting a destination to watch a sporting event, which is one of the three types of sport tourism identified by Gibson (1998). In noting that considerable debate exists in the literature on the definition of sport tourism, Gibson (1998, p. 47) suggests the following working definition of sport tourism: ‘Leisure-based travel that takes individuals temporarily outside of their home communities to play, watch physical activities, or venerate attractions associated with these activities’.

The ability to determine the economic impact of major sport events has been the focus of numerous studies over the years (Burgan & Mules, 2001; Burns & Mules, 1986; Crompton, 1995; Dwyer et al., 2003, Dwyer et al., 2005, 2006; Gratton et al., 2004; Hall, 1992; Shibli & Gratton, 2001; Mules & Faulkner, 1996; Preuss, 2004). While there has been debate over how to best estimate the economic impact of events (e.g. using Input–Output analysis or Computable General Equilibrium Approach), the research on the subject has been of great value to both public and private organizations in communities that are interested in hosting such events. In fact, a number of cities have incorporated sport events into their economic development mix (Chalip & Leyns, 2002), thus increasing interest in the evaluation of their impact. From an economic perspective, the hosting of major sport events has been shown to bring significant economic activity to local industry, and, in particular, to the construction and hospitality industries (Sub-Committee, 1998). These events can also be leveraged by managers in order to increase the interest and involvement of both visitors and residents. Mega-events (Ritchie, 1984) can be used as an opportunity ‘to establish a destination's tourism credential at the international level’ (Croutch & Ritchie, 1999, p. 148). This, in turn, can provide a city (or region) with ‘destination competitiveness’ (see the conceptual model of destination competitiveness in Crouch & Ritchie, 1999). Despite the benefits suggested above, there remains some uncertainty regarding the tourism-related benefits a region experiences as a result of hosting a major sport event.

To resolve this uncertainty, two important steps must be implemented by cities who hope to host a major sport event. First, the complex nature of visitor-generated economic impact must be articulated. This infers that the money entering the region, minus the money that leaves the region through event visitors, has to be identified, and the flow of transactions traced. Second, the resulting visitor-generated primary impact can be inserted into one of the many available models to calculate the overall economic impact such as RIMS (Regional Input–Output Modeling System) (Donnelly et al., 1998; Wang, 1997) or IMPLAN (Impact Analysis for PLANing) (Dawson et al., 1993; Donnelly et al., 1998; Wang, 1997). However, these models need a realistic estimate of the primary impact of a given event, and, as such, may be considered insufficient. Prior to using the models to calculate the economic impact of Games visitors, one must first determine the consumption patterns of these visitors. Given this, it is surprising that consumption patterns of visitors at major sport events have yet to receive much attention in the literature. Empirical research by Crompton (1999) investigated the consumption patterns of tourists at several smaller festivals, including 14 sporting events. Gratton et al. (2000) look at the regional impact of six special sporting events in England. The Sports Industry Research Centre at Sheffield Hallam University coordinated by Coleman (2004) did a study of 17 sport events from 1997 to 2003 in UK. All three of these studies compare the consumption of visitors, officials, athletes, and media, and calculate the economic impact of such events, but they focus solely on small events.

The literature on special sport event spectators is somewhat limited. Delpy et al. (2001) argue that while much has been written and researched around major sport events such as the Olympic Games, the spectator's role is often ignored, particularly when assessed from an economic point of view. Other research, however, suggests that this may change as both ticket prices and the commercialization of major sport events continue to increase (e.g. Seguin et al., 2005). This, in turn, brings special attention to the role of the spectator (Chalip & Leyns, 2002). The vast majority of research on major sport events has focused on the Olympic Games. However, there is a need to expand this view and broaden the analyses of major sport events – and, certainly, there is a dire need to improve our understanding of both spectators and visitors.

Groups to be Considered in Economic Impact Studies

An important component of economic impact calculations involves estimating the number of people who come specifically to see the event and bring ‘fresh’ money into the host city. However, identifying the exact number of visitors at an event can prove to be quite challenging. The 2002 Commonwealth Games in Manchester was no different. Ticket sales numbers and the availability of seats were considered as methods to calculate attendance, but both proved to be insufficient, mainly due to the fact that a large number of people, such as VIPs, media, referees, athletes and team staff, had free access to the facilities. To further complicate measurement, some visitors attended multiple sessions on the same day. Despite these limitations, ‘ticketed Games sessions are estimated to have been attended by 400,000 people and 2% of the UK adult population reported that they were a spectator at the Games, 40% of whom originated from the Northwest … One million visitors came to Manchester over the 10 days of the Games’ (Maunsell, 2004, pp. 17–18). In addition to their limited accuracy, these calculations were also not sufficient to calculate the primary economic impact of visitors because they did not indicate who brought in ‘fresh’ money. It was, thus, necessary to embark on this study and conduct a more detailed analysis of the origin of the spectators attending the 2002 Commonwealth Games.

Prior to presenting results concerning the groups of individuals to be considered for the regional economic impact analysis, a few key terms are presented and defined here.

• Event-affected persons are persons who are attracted by the event (e.g. spectators, workers in tourism industry, etc) and those who avoid the event by leaving or not entering a host city/region because of the event.

• Spectators are persons who attend sessions of the event. They have no work commitments during the event and can be residents, tourists, or day visitors.

• Tourists and day visitors are persons who do not live in the city/region. Tourists stay one night or longer in the host city/region, while day visitors enter and leave the city/region in the same day.

• Residents are persons who live permanently in the city/region.

By presenting a framework, Figure 1 gives a theoretical overview of event-affected persons' movements to and from a host city/region (Preuss, 2005).

Figure 1 Movements of Event-affected Persons during Event-time. Source: Preuss (2005, p. 288).

Preuss (2005) refers to ‘Extentioners’ (A), ‘Event visitors’ (B), and ‘Home Stayers’ (C) as event visitors who spend ‘fresh’ money and create a significant economic impact. The group of residents who avoid the event are distinguished as ‘Runaways’ (D) and ‘Changers’ (F). The Changers are the persons who had planned a holiday trip and decided to change the dates of the trip to coincide with the event. While they were scheduled to be away during the event, they did not carry more money out of the city/region during the event than they would have carried away normally over the year (Preuss, 2005). However, it is suggested that Runaways create opportunity costs. These residents plan an additional trip and leave the city (running away) during the Games. As a result, they spend money out of the region that otherwise would have stayed within the city/region, thus representing an ‘opportunity cost’ to the city. The impact of their behavior may be significant. For example, at the 1992 Olympic Games in Barcelona, it was reported that 16% of the people interviewed six months prior to Games considered spending their holidays outside the city during the event (Brunet, 1993). Similarly, results of the 1998 Traveland Survey suggested that 18% of residents intended to leave Sydney to travel abroad during the 2000 Olympic Games in Sydney (TFC, 1998). Theoretically, the risk to a host city is that Runaways and Changers may discover new holiday destinations and decide to return there again in the future.

Preuss (2005) suggests that the ‘Casuals’ (G) and ‘Time switchers’ (H) be considered in economic impact studies. He argues that while the expenditures of Casuals would occur even without the event, and the Time Switchers would come to the city/region anyway, the two groups spend their money and time during the event on event-related actions rather than on everyday tourist attractions. For example, attendance figures at popular non-Olympic-related tourist attractions were down 30 to 50% during the 1984 Olympic Games in Los Angeles (ERA, 1984). The difference in the consumption patterns of Casuals and Time Switchers during major Games necessitates further and more in-depth studies of event-related economic impact. In addition, it was shown that these groups are more likely to spend additional money due to the event than they would have spent otherwise.

‘Avoiders’ (E) plan a visit to a specific city or region and decide to stay away because of the event, thus representing opportunity cost to the host city. For example, a 1999–2000 Utah Skier Survey found nearly 50% of non-resident skiers indicated that they would not consider skiing in Utah during the 2002 Olympic Winter Games in Salt Lake City. When probed as to their reasons for this, the respondents indicated that crowds (76%) and higher prices (20%) were the primary deterrents to skiing in Utah during the Games (GOPB, 2000). In similar studies, 66% of Danish tourists indicated that they avoided the Lillehammer region during the 1994 Olympic Winter Games (Getz, 1997), and the Costa Brava region of Spain was shown to have lost part of its summer season tourists during the 1992 Olympic Games in Barcelona (Ministerio de Economíca, 1991, 1992, 1993; Ministerio de Industria, 1990). Similar situations occurred during the 1984 Olympics in Los Angeles to theme park hotel owners (ERA, 1984), and during the 1988 Olympic Games in Calgary with skier visits (GOPB, 2000). As indicated in the model, Group E is further divided in two subgroups: the ‘Pre/Post Switchers’ (E2) and the ‘Cancellers’ (E1). The Pre/Post Switchers will come to the city/region but change the time of their visits to pre- or post- event. On the other hand, the Cancellers will cancel their visit entirely. Preuss (2005) also argues that new tourists and MICE tourists (Meetings–Incentives– Conventions–Events) that are attracted by a major sport event will likely overcompensate through increased consumption for both subgroup E1 and those who lose their interest in traveling to the host city/region due to a subjective negative image of the event.

Method

A 49-item questionnaire was developed to investigate a number of sport event tourism issues. The analysis of the data from this study is expected to contribute to the literature by improving the methodology in order to better predict visitor impact during major sport events. The questionnaire was developed following an in-depth assessment of previous instruments and a comprehensive review of the scientific literature. An instrument developed for investigating small events by Gratton et al. (2000) was particularly important to this study, especially for items concerning the consumption patterns of visitors. The method of examining consumption patterns by interest also exists in the literature (Deery et al., 2005). The current research goes a step further by seeking to identify the consumption patterns that are relevant to the study of economic impact. Here, an initial examination of the spending intentions of visitors provides the basis from which to decide whether or not event-affected persons provide ‘fresh’ money to the region. To measure this, the questionnaire was designed to assess visitors by the intention of their visit (see Figure 1). Other areas important to segmentation were: (a) the number and type of visitors at the Commonwealth Games; (b) the number and prices of tickets purchased; (c) interest in other cultural attractions or trips before and after the Games; (d) length of stay; (e) group visit; and (f) visitor accommodations while in Manchester.

The 2002 Commonwealth Games was one of the largest sporting events in the world with a sport program that included 17 sports and 3,690 participating athletes representing 72 nations. The event's official website reported that ‘around one million visitors are thought to have come to Manchester to see the event live and the world television audience was estimated to top one billion’ (Manchester, 2002). Data collection took place during the 2002 Commonwealth Games in the four day period between 2 August and 5 August 2002. The research team consisted of six sport management university students. Training sessions were conducted prior to leaving for Manchester and some of the researchers had previous field research experience from the 2002 Olympic Games in Salt Lake City. The researchers disbursed across 15 different locations to prevent bias from profiling only one type of spectator. The questionnaires were administered in English and the data were analyzed using SPSS.

Results

The results section includes demographic information on the respondents of this study. Next, an analysis of the different groups that should be considered in estimating economic impact will be presented. This will be followed by an examination of the consumption patterns of multi-sport-event visitors.

From the 1,800 administered surveys, a total of 1,196 (66.4%) usable surveys were achieved, with 51% of respondents being male and 49% female. Figure 2 outlines the distances traveled by respondents to attend the 2002 Commonwealth Games. It is important to note that these data refer to distance traveled from last destination visited and not necessarily the visitor's home country.

Figure 2 Distance Visitors had to Travel to Manchester (n = 941).

Further, 91.3% of respondents came from the United Kingdom, meaning that only 8.7% of visitors traveled from overseas destinations such as Australia (2.7%) and New Zealand (1.9%). It is, however, important to note that 21.3% of respondents did not answer the question related to distance traveled to the Games. Although we cannot provide a precise reason for why this occurred, it is possible that the visitor was not sure about how far they traveled, or perhaps they did not have to travel any significant distance to attend.

The respondents' ages ranged from 16 to 82 years, with a median age of 26, a mean of 40.6 years and a standard deviation of 15 years. Figure 3 provides the average age of respondents by locations investigated.

Figure 3 Average Age of Respondents by Sport Venue (Mean 40.6).

Further investigation of the data provided in Figure 3 reveals significant differences in the age of spectators attending different events. For example, Field Hockey (t = −2.9; p < 0.05), Netball (t = −17.85; p < 0.001), and Lawn Bowls (t = 8.77; p < 0.001) showed significant differences by age, with Lawn Bowl spectators being significantly older while Field Hockey and Netball spectators were significantly younger. To further emphasize the point that different sports attract different spectator groups, a multiple logistic regression model using eight independent variables was conducted. These variables describe the spectators from very different perspectives. We used ‘duration of stay’, ‘cost of accommodation’ (shows wealth), ‘gender’, ‘age’, ‘with whom did you attend’ (shows group interest), ‘budget spent on merchandise’ (shows identification with Games/Sports), ‘interest to attend the Games to rest and relax’ (shows other interests beyond sport), and ‘interest to attend the Games to see sport’ (shows particular sport interest) was run against four selected sports (Field Hockey (n = 129), Badminton (n = 164), Rugby (=440) and Lawn Bowls (n = 143)). The resulting model showed many variables which significantly split the groups. The regression model noted that the eight factors as a group are a good predictor for Rugby visitors (85.7%), while Lawn Bowls (51.5%), Field Hockey (33.3%), and Badminton visitors need to be segmented by other variables.

Figure 4 shows the gender of visitors by location where the majority of data was collected. Only Netball showed significant differences (χ² = 4.62; df = 1; p < 0.05) in gender, predominantly female in this sample (n = 143).

Figure 4 Sport Venue and Gender of Respondents.

An important objective of this research is to identify those groups who drive the primary economic impact of a sporting event. In this regard, results demonstrate that all groups (i.e. Home Stayers, Time Switchers, Casuals, Games Visitors, Extentioners, and Residents) attended the Commonwealth Games in Manchester 2002 (see Table 1). Note that we opted to substitute the group label ‘Event Visitors,’ as used by Preuss (see Figure 1), with ‘Games Visitors’ since our study is related specifically to the Commonwealth Games.

Table 1  Groups of Visitors to the 2002 Commonwealth Games in Manchester

Per cent in this survey	Group related to Figure 4
15.8%	(C)	Home Stayers: ‘I am resident and abstained from my vacation in order to attend the Games’
4.9%	(H)	Time Switchers: ‘I am a tourist who wanted to travel to Manchester anyway, this year or next year’
17%	(G)	Casuals: ‘I am a tourist and would have visited Manchester even without the Games’, and ‘I am a tourist who wanted to travel to Manchester anyway this year or next year’
46%	(B)	Games Visitors
	(A)	Extentioners
16.8%	(K)	Residents: Citizens living less than 50 km from main stadium

Our results indicate that less than 50% of the respondents were Games Visitors (A) and Extentioners (B). This finding supports that the proportion of visitors who are actually visiting the host city to attend the Games is often over-emphasized in economic impact studies.

For the Games in Manchester, event visitors can be segmented into three groups based on the economic impact they bring to the region. The first group did not bring ‘fresh’ money to Greater Manchester. Their contribution involved a re-allocation of leisure consumption they would have expended anyways (E2, G, H and K). In economic impact study, this segment is considered only if certain visiting groups spent additional money above and beyond their normal tourist consumption patterns or changed their spending patterns, or if residents changed their long-term spending patterns or propensity to consume. These differences in behaviors would result in a different macroeconomic impact. The second group includes those who bring ‘fresh’ money into Greater Manchester (A, B, C). While A and B come as tourists, group C are the Home Stayers who do not vacation outside of the region. Here, it is important to consider the money they would have spent abroad (known as import substitution). The third group is comprised of individuals who do not spend money that would have been spent in the Greater Manchester if the Commonwealth Games had not occurred. This can be considered as an opportunity cost for the region, which would then need to be subtracted from the event impact (E1, D). As an equation, the three groups form the additional input into a region as follows:

Consumption Patterns of Multi-sport Event Visitors

Most economic impact studies of sport events include the consumption information of ‘normal’ tourists to a host city. To demonstrate that this may be an incorrect approach, the current study shows that there are significant differences in the consumption patterns of tourists (n = 573), day visitors (n = 240), and the residents of the host city (n = 380). Government research on visitors to Northwest England in 2002 showed that they stayed an average of 4.3 days and spent £45.24 per day (UK Industry, 2005). In comparison, the results of the current research suggest that visitors to the Commonwealth Games spent between £79.3 (staying with friends or relatives) and £196.4 (staying at a four to five star hotel) per day. In both cases, visitors to the Games spent significantly more than the ‘normal’ visitor to the region at other times.

If we remove accommodation from the equation, daily expenditures are found to be quite similar for all tourists – from an average low of £76.9 (Bed & Breakfast/Youth Hostel) to an average high of £113.3 (four to five star hotel). Cluster analysis reveals that there is only one main cluster of tourists, as well as two small groups we called ‘Extreme Luxury Stayers’ and ‘Heavy Shoppers’. If we compare ‘tourists’ to ‘day visitors’ and ‘residents’, we are able to determine whether the respondents from each group (tourists, day visitors, residents) felt they had changed their consumption during the Games in comparison to other holidays or to their typical daily expenditures when at home. Figure 5 summarizes how ‘tourists’ felt they had changed their consumption during the Games by showing how they compared their Games experience to other normal holiday consumption experiences.

Figure 5 Comparison of Consumption of Tourists during the Games and during other Holidays.

Our findings suggest that tourists ate more fast food (t = 3.42; p < 0.001) and used more public transportation (t = 4.46; p < 0.001) during the Games as compared to other holidays. However, they also ate less often in restaurants (t = −10.64; p < 0.001), did less shopping (t = −10.17; p < 0.001) and visited fewer ‘normal’ tourist attractions (t = −13.29; p < 0.001).

The ‘day visitors’ also showed significant differences in their consumption patterns. One can note that the curve is shifted to the left when compared to that of the ‘tourists’ (see Figure 6).

Figure 6 Comparison of Consumption of Day Visitors during Games and during other Holidays.

Day visitors ate less fast food (t = −2.28; p < 0.05) and went to restaurants less often (t = −12.03; p < 0.001). It seems that day visitors often brought their own food to Manchester. This group also did less shopping (t = −8.78; p < 0.001) and visited normal tourist attractions less often (t = −12.12; p < 0.001). This can be explained by the fact that day visitors spent most of their time at the events (purpose of the trip), thus leaving little time for other activities.

Residents were also asked about the changes in their consumption during the Games versus their average daily consumption (see Figure 7).

Figure 7 Comparison of Consumption from Residents during Games and during Daily Life.

Significant differences were found in two categories. Residents ate more fast food (t = 6.0; p < 0.001), and used public transportation more often than they did in their normal daily lives (t = 15.84; p < 0.001). However, it is important to note that public transportation was free for Games ticket holders as parking capacity was limited. Thus, the increase in public transportation should not be surprising.

Overall, these results strongly suggest that tourists, day visitors, and residents have different consumption patterns during major sport events compared to during other holidays, or during their normal daily lives.

To this point, we have distinguished the event-related persons as tourists, day visitors and residents. The next step in our analysis is to determine whether there are other variables that may split the group of visitors into subgroups with different consumption patterns. This approach would mean a more precise evaluation of the economic impact, and improve the ability to establish effective marketing strategies aimed at specific target groups. The data is analyzed in two ways: (i) the visitors' outlay (or opportunity costs) to attend the Games, and (ii) the distance the visitor traveled to Manchester.

The streams of persons (see Figure 1) leaving the city (D), or Crowded-Out Visitors (E1), are often overlooked in economic impact studies but often create considerably high opportunity costs. They could not be measured by our survey during the Games (see more in Preuss, 2005, p. 292). However, results allowed for the segmentation of the visitors into four groups based on their interest in the Commonwealth Games.

• (1) ‘Residents’ (K): This is a group that visits the Games in their neighborhood. It is assumed that the majority of individuals making this group would not have traveled to Commonwealth Games when hosted in other countries. This group has both low costs to attend the Games and low opportunity costs resulting from the Games.

• (2) ‘Casuals’ (G) and ‘Time Switcher’ (H). These groups are visitors who are in Manchester or wanted to visit Manchester at some point, regardless of the Commonwealth Games. Therefore these two groups do not purely come to Manchester because of their interest in the Games. Their outlay to attend the Games is not very high since they benefit from non-Games attractions and entertainment.

• (3) ‘Home Stayers’ (C). These are also citizens of Manchester who have forgone their typical holiday to another location in order to attend the Games. This is an import substitution. Although their outlay to attend the Games is not high, they represent high opportunity costs for the Home-Stayer as they forego their normal holiday by preferring to stay for the Games.

• (4) ‘Games Visitors’ (B) and ‘Extentioners’ (A). These are the real Games visitors who came to or stayed in Manchester as a result of the Games. These groups spend considerable amounts of money to attend the Games, which indicates a high preference for the Commonwealth Games.

Based on the categorization above, it can be shown that the groups have different consumption patterns (see Table 2).

Table 2  Consumption Patterns of Visitors with Different Motivation to see the Games

	Group 1	Group 2	Group 3	Group 4
	Residents	Casuals & Time Switchers	Home Stayers	Games Visitors & Extentioners
Outlay (or opportunity costs )	low	medium	high	very high
Respondents	n = 166 (16.8%)	n = 222 (21.9%)	n = 160 (15.8%)	n = 466 (46%)
Ø number days	2.25 (n = 162)	3.04 (n = 212)	2.77 (n = 156)	3.16 (n = 459)
Ø number tickets/day	1.1 (n = 165)	1.05 (n = 214)	1.26 (n = 159)	1.24 (n = 456)
Ø spending merchandise	£21.41 (n = 40)	£15.44 (n = 138)	£20.00 (n = 29)	£27.74 (n = 340)
Ø spending tickets	£86.93 (n = 29)	£64.87 (n = 153)	£74.88 (n = 34)	£89.65 (n = 365)
Attended with family	60.9% (n = 165)	47.7% (n = 215)	59.4% (n = 159)	58.2% (n = 459)
Significance: relaxation	N/A	2.49 (n = 179)	N/A	2.24 (n = 401)
Significance: sports	N/A	3.58 (n = 177)	N/A	3.55 (n = 400)
Significance: cultural events	N/A	2.23 (n = 196)	N/A	1.81 (n = 433)
Significance: travel UK	N/A	2.46 (n = 178)	N/A	1.94 (n = 401)

On average, the respondents (n = 1,196) attended the Games for 2.9 days. Residents (group 1) attended the Games for a shorter time period at 2.3 days (t = −6.04; p < 0.001), while Games Visitors (group 4) attended for a longer period at 3.2 days (t = 2.09; p < 0.05). The hypothesis that those who have higher outlay attended more days than those who were living in Manchester proved to be true. ANOVA was used to perform further analysis of the variables with standard distribution, and non-parametric tests such as the Mann-Whitney-U-Test and Kolmogorov-Smirnov-Z were used to analyze variables with non-standard distribution. The tests revealed only a few significant differences between the four groups. Home Stayers (group 3) and Games Visitors – those with higher outlay – purchased significantly more tickets than did Residents. Further, Games Visitors did significantly more shopping than did Casuals (group 2). When analyzing Residents and Games Visitors, we note that the differences in the motivation to attend the Games are reflected in the willingness to spend more money and time on the Games. On average, Games Visitors spent significantly more money for tickets, and, when compared against the Casuals, they spent significantly more on merchandising. All these findings support the hypothesis that groups that accept higher outlay only do so because they expect event-related benefits in return. This expectation translated into longer stays in the host city, and increased spending on tickets and merchandise. Other findings suggest that visitors were also attracted by the cultural activities that ran parallel to the main sport event. These activities included the Culture Shock, the Spirit of Friendship Festival, Queer Up North and a comedy festival. Three sites in Castlefield, Exchange Square and Great Northern Square were established for live broadcasts of the Games Opening and Closing Ceremonies. We find that Casuals assigned significantly more importance to participating in cultural events than did Games Visitors (F = 8.52; p < 0.001). It also appears that Casuals were more prone to travel before and/or after the Games in the UK (F = 9.57; p < 0.001). Both results were not surprising, since Casuals were in Manchester for reasons not related to the Commonwealth Games.

When examining the social facet of the Games, it was discovered that only 3% of spectators attended the Commonwealth Games alone. Casuals and Time Switchers attended the Games with friends, while, for the most part, all other groups attended with family members. In general, the varying visiting pattern was highly significant among the groups (χ² = 26.45; df = 6; p < 0.001). Finally, it appears that the Home Stayers (group 3) and the Games Visitors and Extentioners (group 4) had a greater interest in the Games. In fact, they bought more tickets and merchandise than did the Casuals and Time Switchers (group 2) and Residents (group 1). Their increased interest in the Games is further suggested by their higher outlay.

We also conducted an empirical analysis of consumption patterns of visitors by distance traveled to Manchester (Table 3).

Table 3  Consumption Patterns of Visitors with Different Distances from Home to Manchester

	Group 1	Group 2	Group 3	Group 4	Group 5
	Residents and Home Stayers	Day visitors	Northern UK	Rest of UK	Foreign country
Distance traveled	0–30 miles	31–150 miles	151–270 miles	> 271 miles	By airplane
Respondents	n = 128 (13.6%)	n = 338 (35.9%)	n = 324 (34.4%)	n = 69 (7.3%)	n = 82 (8.7%)
Ø number days	2.34 (n = 125)	2.33 (n = 328)	3.28 (n = 316)	4.04 (n = 67)	4.82 (n = 79)
Ø number tickets/day	1.31 (n = 126)	1.39 (n = 327)	1.16 (n = 320)	1.17 (n = 65)	1.00 (n = 80)
Ø spending merchandise	£18.8 (n = 60)	£21.89 (n = 229)	£24.91 (n = 243)	£31.69 (n = 49)	£37.09 (n = 53)
Ø spending tickets	£71.44 (n = 77)	£77.17 (n = 252)	£83.19 (n = 260)	£123.65 (n = 54)	£106.36 (n = 59)
Attended with family	54.7% (n = 128)	59.2% (n = 331)	54.7% (n = 316)	56.7% (n = 67)	45.6% (n = 79)
Significance: relaxation	2.21 (n = 71)	2.34 (n = 272)	2.35 (n = 282)	2.12 (n = 60)	2.35 (n = 69)
Significance: sports	3.36 (n = 78)	3.50 (n = 303)	3.67 (n = 310)	3.70 (n = 64)	3.50 (n = 76)
Significance: cultural events	1.87 (n = 67)	1.88 (n = 271)	1.91 (n = 278)	1.97 (n = 60)	2.22 (n = 68)
Significance: travel UK	2.04 (n = 67)	1.92 (n = 273)	2.08 (n = 279)	2.05 (n = 59)	3.04 (n = 74)

Results suggest that the number of days attended by visitors at the Games increased by distance traveled to the Games. Similarly, the ANOVA analysis indicates that average spending per day for merchandise and tickets appeared to increase in relation to the distance traveled as a significant difference (ANOVA) was found on the amount spent on tickets between group 4 and groups 1 to 3. The analysis on merchandising did not show significant differences, likely as a result of the high standard deviations of this data. Non-parametric tests for this variable did not show differences as well. Simple correlation analysis indicates a significant correlation between consumption and distance traveled. The greater the travel distance to the Commonwealth Games, the more money was spent on tickets (r = 0.12; p < 0.05). The greater distance traveled also led to an increase in the number of tickets purchased (r = 0.199; p < 0.001), as well as significantly longer stays in the city (r = 0.319; p < 0.001). With the exception of Overseas Visitors (group 5), over 50% of the members of each group traveled to the venues with family members. This demonstrates the importance of social factors in the consumption patterns of visitors at major sporting events. It was not surprising to find that Overseas Visitors were significantly more interested in traveling within the UK before and/or after the Games (F = 16.52; p < 0.001) than residents of the UK.

Conclusion

The empirical findings of this study, which profiled visitors of the 2002 Commonwealth Games in Manchester, provide the following results:

• (1) Calculating the economic impact that results from the consumption of Games-affected persons is very difficult. Many different groups must be considered in order to detect the overall consumption impact. For example, opportunity costs of crowded-out visitors and residents that leave the city during the Games are complex to measure but still must be considered.

• (2) Games Visitors to the Commonwealth Games can be segmented into different groups. As shown with Lawn Bowls, Netball and Field Hockey the spectators differ significantly in age and gender. Our regression analysis suggests that it may be possible to predict the type of visitors/spectators at different sporting events. This information could be used to develop targeted marketing strategies designed to draw the different groups to specific sporting venues. More research should be conducted over time to refine this model.

• (3) Even if we measure subjective appraisals, the aggregation of data shows that Games Visitors consume differently during Games than they do during normal holidays. Furthermore, we noted that Residents make changes in their consumption patterns during the Games. This should be further explored in future economic impact studies.

• (4) The consumption pattern of spectators is dependent upon their outlay (opportunity costs). Those that have higher outlay show a greater intention to attend the Commonwealth Games and therefore spend more time in Manchester, and buy more tickets and merchandise. It is not surprising that we also found significant differences, other than consumption, among those visitors who wanted to travel to Manchester even in the absence of the Games. They demonstrated greater interest in visiting cultural events or adding a trip in the UK before or after the Games than those who came only to see the Games.

• (5) The consumption pattern was also correlated to the distance traveled by spectators to attend the Commonwealth Games. The greater the distance a visitor traveled, the more time and money they spent in the city. Therefore, it may be economically wise to attract as many foreign visitors as possible for to major sport events.

• (6) The cluster analysis on the consumption pattern did not show many strong clusters. Only one cluster dominated, revealing that the pattern of spectators (food, tickets, transport, shopping, entertainment, others) was dominated by large standard deviation. Thus, for future research, an economic impact analysis could work with an average consumption pattern of Games visitors. The significant differences in money spent on accommodations should also be considered. As a result, the different groups may be built into an analysis using accommodation categories.

The importance of emphasizing the consumption patterns of residents and tourists in forecasting the economic impact of major sport events was revealed through this study. When political decisions to host or not to host a major sport event are based on studies that do not correctly estimate the visitors' impact, there may be two consequences: first, public authorities might decide whether or not to bid for an event based on false assumptions; second, the local tourism industry might fail to prepare adequately for the consumption needs of their customers.

Overall, this research makes three important contributions to the literature. First, it provides empirical evidence that different sports attract people of different ages and genders. This means that event hosts must be aware of the visitor demographics they attract and adjust their targeted marketing efforts accordingly. Second, an important clarification for economic impact study is highlighted; namely, that it is inappropriate to the potential impact of visitors without first carrying out a segmentation exercise. Finally, and importantly, the need for improved metrics for economic impact study was clearly identified. Specifically, it was noted that there is a need for metrics that measure economic impact while considering segmentation effects.

In terms of future research, it is suggested that the consumption patterns of visitors be evaluated at other major events. The results of this research also demonstrate how visitors at major sport events are also the physical audience at the events and produce an atmosphere that appeals to television audiences and broadcasters. As seen at the 2004 Olympic Games in Athens and the 2006 Olympic Games in Turin, small live audiences decrease the special aura of excitement at the Olympic Games. A large television audience is a key element in maximizing revenues from the sales of broadcasting. However, revenues from broadcasting rights do not necessarily lead to financial benefits for the host city. For the host community, increased visitor consumption during the Games is a key factor to its financial success.