Quality of Life,Event Impacts,and Mega-Event Support among South African Residents before and after the 2010 FIFA World Cup

Abstract

The purpose of the study was to explore the role of mega-event impacts on perceived satisfaction with quality of life and support among South African residents before and after the 2010 FIFA World Cup. Limited research has empirically tested whether quality of life (QOL) is perceived as an exchange benefit that facilitates resident support of mega-events. Intercept data were collected from residents in five host cities three months before (March 2010) and eight months after (March 2011) the event (N = 3,789). Results indicate significant differences in perceived impacts before and after the event. Before the event, the influence of political impacts, psychological impacts, and social benefits on perceived QOL was significant, while QOL mediated the relationships between political, psychological, and social benefit impacts and resident support. After the event, economic impacts emerged as a significant predictor of QOL in contrast to the preevent sample.

Keywords

mega-events impacts residents quality of life FIFA World Cup South Africa

Introduction

Mega sport events (e.g., Olympic Games, World Cup) are frequently cited as major catalysts, which can yield a number of notable infrastructure development impacts for the host city and nation (Fredline, Jago, and Deery 2003; Kaplanidou and Karadakis 2010; Lee, Lee, and Lee 2005; Swart and Bob 2007). These events are also viewed as political and social mechanisms through which reconciliation, cohesion, and national pride are alleged to manifest (Hiller 2000; Waitt 2003; Zhou and Ap 2009). A case in point for these assertions was the recent 2010 Fédération Internationale de Football Association (FIFA) World Cup in South Africa. This was the first World Cup on African soil and was quickly branded by the South African government as an investment in “Nation Building” (Alegi 2008). As a result, the event was viewed as a driver for social cohesion, economic mobility, and developmental outcomes in a country that offered fertile ground for such advancements (Swart and Bob 2007; Theron 2008; Van Wyk 2008; Walker et al. 2012). In addition, the South African government endeavored to position the nation as a potential host for the Summer Olympic Games, attempting to further facilitate the aforementioned benefits (Cornelissen and Swart 2006; Rogerson 2009).

The support of local residents is important as they can be key stakeholders in mega sport event planning (Sautter and Leisen 1999). Residents provide volunteer services, create the local event atmosphere, and interact directly with consumers of such events, namely spectators, athletes, and other event stakeholders (Jones 2001). Such interaction can directly or indirectly affect their quality of life (QOL). The underlying interaction of the individual with its environment is evident in the definition of QOL by the World Health Organization (1997) as “individuals’ perception of their position in life in the context of culture and value system and in relation to their goals, expectations, standards and concerns. It is a broad ranging concept affected in a complex way by a person’s physical health, psychological state, level of independence, social relationships, and their relationships of salient features of their environment” (p. 1).

Additionally, the evaluation of event outcomes may not be static but rather dynamic, whereby residents’ evaluations change with the advent of time (e.g., Kim, Gursoy, and Lee 2006). Prior to the event, resident perceptions about certain event impacts may differ, compared to those after the event (Hiller and Wanner 2011; Kim and Petrick 2005). For example, in a recent study on the Vancouver Winter Olympic Games, Hiller and Wanner (2011) found that the resident “interaction” with the Games changed after the event (compared to preevent) as the hosting experience increased the evaluation of the event hosting being “worth it” (p. 891). Hiller and Wanner’s (2011) discussion of “worth it” (suggesting support toward the mega event) is a valid one but it does not consider how the perceived event impacts influence residents’ QOL. The understanding of event impacts’ influence on QOL is especially germane in developing nations like South Africa given the country’s limited financial resources that usually targets the improvement of the life of local people. Therefore, examining how mega-event impacts influence QOL and in turn how QOL influences resident support for the event is needed.

The purpose of this study was twofold: (1) to assess differences in event impact perceptions, overall satisfaction with QOL, and event support of host city residents before and after the 2010 FIFA World Cup in South Africa and (2) explore the relative influence of event impacts on residents’ perceived satisfaction with QOL and whether QOL mediates the relationship of event impacts and support before and after the event.

Resident Support and Mega-Events

Much of the extant literature on mega-events has examined resident attitudes toward tourism development (e.g., Gursoy and Rutherford 2004) or event support (e.g., Gursoy and Kendall 2006; Prayag et al., forthcoming). Research has generally utilized Social Exchange Theory (Emerson 1976) to explain the nuanced reactions and perceptions of residents toward a tourism stimuli. Within this framework, social, cultural, economic, and environmental impact variables have been tested as contributing factors to resident support for tourism development (Gursoy and Rutherford 2004) and event support (Gursoy and Kendall 2006; Twynam and Johnston 2004). However, and despite the use of Social Exchange Theory for the predictive validity of attitudes toward tourism development, the theory lacks explanatory power with other concepts such as those identified in intergroup relations (Ward and Berno 2011). Nevertheless, it is not the objective of this study to extend or test boundaries of the theory, but rather to examine whether there is a dynamic evaluation of impacts over time, and if the overall perceptions of satisfaction with QOL are treated as the exchange benefit. In other words, if these impacts bring about an increase in QOL, then residents would perceive the increase in QOL as a benefit, thereby leading to increased support for a hosting decision. Hence, the use of Social Exchange Theory provides a basic framework to explore relationships between impacts and satisfaction with QOL and event support.

The underlying assumption of Social Exchange Theory is that benefits received from tourism development will positively influence resident support for tourism, because of economic, social, and environmental gains. However, research has shown that even without these perceived gains, support for tourism can still be evident (Andereck and Vogt 2000). Interestingly, Andereck and Vogt (2000) identified that perceived QOL was a significant predictor of support toward tourism development for some communities. However, when negative tourism impacts were included in the model, QOL lost its predictive power. Hence, it may be plausible that QOL is a mediator of the relationship between tourism impacts and support for tourism development as improved QOL is likely perceived as the exchanged benefit. This supposition was also indirectly suggested by Andereck et al. (2005), who purported that “tourism is widely perceived as a potential economic base, providing elements that may improve quality of life such as employment opportunities, tax revenues, economic diversity, festivals, restaurants, natural and cultural attractions, and outdoor recreation opportunities” (pp. 1056-57).

In a mega sport event context, Gursoy and Kendall (2006) proposed a model predicting resident support toward hosting, which incorporated variables such as perceived benefits and costs, community concern, and attachment. However, the authors failed to include QOL as a dependent variable, which was somewhat surprising since this outcome is expected (and promoted) by event organizers as contributing to a positive event legacy. Overall, the preceding discussion reveals that perceived changes in QOL may be part of the Social Exchange process, which helps intrinsically (and perhaps extrinsically) motivate residents to support event hosting. It is also important to note that Social Exchange Theory is dynamic considering that over time, residents can reevaluate the exchange process and adjust their feelings accordingly (Waitt 2003) after experiencing the outcome (Hiller and Wanner 2011). Therefore, the evaluation of residents’ perceived benefits and costs may not only occur before the event (i.e., benchmarking of expectations) but also after the event (i.e., long-term evaluation) when residents have experienced the benefits and costs.

Mega-Event Impacts

Several event scholars have examined a wide spectrum of economic and social mega event impacts (e.g., Kasimati 2003; Solberg and Preuss 2007; Waitt 2003; Weed 2008; Zhou and Ap 2009). More recently, research has focused on the conceptualization and measurement of residual impacts (i.e., legacies) of mega-events on a host destination (Preuss 2007). However, since the event legacy discourse can have different meanings, both in terms of context and culture (Olympic Studies Centre 2003; Kaplanidou 2011), the creation of a global definition of the construct has proven challenging. Nevertheless, researchers agree that tangible and intangible outcomes are fundamental to legacy discussions, with the tangible outcomes seemingly assuming a higher profile compared with their intangible counterparts (Olympic Studies Centre 2003; Kaplanidou and Karadakis 2010; Preuss 2007; Waitt 2003). Nevertheless, intangible outcomes are gaining in importance (Kaplanidou 2012).

In the sport and tourism literature, the terms impact and legacy have been regularly used interchangeably, with legacy being defined as “planned and unplanned, positive and negative, intangible and tangible structures created through a sport event that remain after the event” (Gratton and Preuss 2008, p. 1924). The idea of tangible outcomes is similar to the term “hard event structures” coined by Preuss (2007), who also proposed the term “soft event structures” to account for intangible legacy deliverables. The “hard” structures include primarily infrastructure-related projects, while “soft” structures include outcomes such as knowledge development, governance reform, or sociocultural changes related to attitude formation and change of local people (Kaplanidou 2012; Kaplanidou and Karadakis 2010; Preuss 2007; Waitt 2003).

When speaking of tangible impacts, tourism impacts fall under this realm and are examined under economic, sociocultural, and environmental lenses (Andereck et al. 2007; Andereck and Vogt 2000; McGehee and Andereck 2004; Meyer 2011). The net economic gain, minimal impacts to everyday life, recreation infrastructure development, beautiful environments, positive interactions between residents and tourists, understanding and tolerance for community/culture, and inclusion of local residents in the decision-making process have an impact on the economic, social, and environmental orientation (Prayag et al., forthcoming; Haralambopoulos and Pizam 1996; Liu and Var 1986; Lindberg and Johnson 1997). Andereck and Nyaupane (2011) found residents held more positive attitudes toward tourism because of recreation amenity availability and feelings of community pride. Residents also indicated that tourism had a positive impact on the economy, preserved facilities of natural and cultural resources, enhanced community well-being, and affected the overall community’s way of life (Andereck and Nyaupane 2011). Negative impacts are also related to tourism, which include the issues of crowding, traffic, parking problems, increased crime, and cost of living, which all can pejoratively influence QOL (Andereck et al. 2005; Ap and Crompton 1993; McCool and Martin 1994).

Also notable in this discussion are the negative impacts from mega-events, which are similar to negative tourism impacts. The implications from the negative mega-event impacts can (potentially) be more far-reaching for the destination and/or host country. For example, antisocial behavior, increased crime, congestion, crowding, parking, community alienation and displacement, administrative problems, security, and overcommercialization (Andereck et al. 2005; Bull and Lovell 2007; Gursoy and Kendall 2006; Jones 2001; Owen 2005) can all significantly and negatively influence QOL. In some cases, opportunity costs can create a negative displacement effect of funds for the host community (Kaplanidou and Karadakis 2010). Hritz and Ross (2010) recently reported that negative social costs had an adverse impact on residents’ QOL. Therefore, preparing for these negative realities, while diligently working to assuage them, could provide another means toward maximizing the effects of hosting a mega-event such as the FIFA World Cup.

Pre- and Post-Event Impact Studies

While researchers have examined pre- and post-event impacts (e.g., Bob and Swart 2009; Collins, Calvin, and Munday 2009; Jin et al. 2011; Gursoy et al. 2011; Kim, Gursoy, and Lee 2006; Kim and Morrison 2005), the results remain inconclusive regarding positive and lasting societal yields for the host communities. This generalization is due (in part) to a lack of evidence supporting long-term changes in host societies. In fact, a systematic review of social impact studies (see McCartney et al. 2010) supports this caveat of inconclusiveness. The limited evidence for mega-events as social “intervention” mechanisms has also been questioned by Weed (2010) and Murphy and Bauman (2007) whose studies focused on social changes related to enhanced physical activity among host nation residents. Mega-events require a significant amount of resources from a host country to be successful (Toohey and Veal 2007), and one such resource is resident support. Residents not only welcome and host visitors but also endure positive and negative changes due to the preparations for the event (Gursoy and Kendall 2006). All these changes could significantly influence QOL and the perceived impacts of the event on the host community.

Quality of Life as a Mediator between Impacts and Support

While the topic of resident attitudes and perceptions toward expected event impacts has been adequately researched (Bob and Swart 2009; Bull and Lovell 2007; Gursoy and Kendall 2006; Preuss and Solberg 2006), there is a paucity of research with respect to the impact of events on QOL. However, some community-level work on QOL does exist, which may be relevant or lend a theoretical structure to the mega-event context primarily because of the exchange of experiences between local residents and sport tourists. For example, Chhabra and Gursoy (2009) found that sociocultural impacts such as social interactions with tourists can explain an improvement to QOL, a phenomenon existing in the mega-event framework. Similarly, Nawijn and Mitas (2012) found that perceived tourism impacts improved life satisfaction.

Similarly, Karadakis and Kaplanidou (2012) found that residents of the host city of the 2010 Vancouver Olympic Winter Games valued economic, environmental/infrastructure, and sociocultural legacies for their QOL. In the same study, nonhost city residents were also examined. The nonhost sample indicated that psychological legacies were most important for their QOL as a result of hosting the Vancouver Olympic Games. The latter study also affirms previous research that discussed the potential of the Olympic Games to provide the host city with opportunities to showcase tourist attractions and new infrastructure, which indirectly signals improved QOL (Chalip 2002; McGehee and Andereck 2004; Owen 2005; Solberg and Preuss 2007; Whitson and Horne 2006) and concurrently supports the importance of intangible impacts. Similar findings by Nichols, Giacopassi, and Stitt (2002) suggest an increase in QOL as a result of improved socioeconomic conditions. Andereck et al. (2007) also found that residents felt tourism should increase their QOL, as it was “essential for residents’ satisfaction with their community, personal lives, activities and environment” (p. 498).

QOL has been shown to include both objective (i.e., conditions of life) and subjective (i.e. experiences of life) aspects (Osborne 1992). Definitions of QOL suggest that it is a multidimensional concept composed of socially and culturally related factors (e.g., life satisfaction, happiness) (Schalock et al. 2002). Quality of life indexes exist in the literature along with single-item measures that evaluate the overall satisfaction with a person’s life (Diener 2000; Hagerty et al. 2001). In tourism research, single- and multi-item questions have been used to measure QOL as an attitude toward tourism development. The difference between QOL and attitude/impact studies is essentially one of measurement. Andereck et al. (2007) indicated that “attitude/impact studies largely focus on the way in which tourism is perceived to effect the communities and the environment, whereas quality of life studies are typically concerned with the way these impacts affect individual or family life satisfaction, including satisfaction with community, neighborhoods and personal satisfaction” (p. 485). A review of the literature illustrates that attitude/impact studies tend to ask respondents to indicate their level of agreement or disagreement with statements that emphasize impacts on their community “without specific questions linking these impacts with influences on individuals’ quality of life” (Andereck et al. 2007, p. 485).

In sum, this study aims to extend previous research by answering the following research questions that guided this investigation:

Research question 1: Are there any differences in residents’ perceptions of mega event impacts, overall satisfaction with QOL and event support before and after the event?

Research question 2: What is the influence of specific event impacts on overall satisfaction with QOL, and does the latter mediate the relationship between event impacts and event support?

Additionally, the specific hypotheses below (see Figure 1) examine the relationships presented in research question 2 between the different impact types, satisfaction with QOL, and event support and were formulated on the basis of the previous literature review:

Figure 1.

Model tested in this study that examines the influence of mega-event impacts on perceived overall satisfaction with quality of life and overall event support before and after the event.

Hypothesis 1: Economic impacts will positively influence overall satisfaction with QOL.

Hypothesis 2: Tourism impacts will positively influence overall satisfaction with QOL.

Hypothesis 3: Political impacts will positively influence overall satisfaction with QOL.

Hypothesis 4: Psychological impacts will positively influence overall satisfaction with QOL.

Hypothesis 5: Infrastructure impacts will positively influence overall satisfaction with QOL.

Hypothesis 6: Opportunity and social costs will negatively influence overall satisfaction with QOL.

Hypothesis 7: Social benefits will positively influence overall satisfaction with QOL.

Hypothesis 8: Overall satisfaction with QOL perceptions will positively influence resident support for the 2010 FIFA World Cup.

Method

Data were collected from residents of five (Rustenburg, Johannesburg, Pretoria, Nelspruit, and Polokwane) of the nine host cities in South Africa before and after the 2010 FIFA World Cup. Data collection took place three months before the event (March 2010) and eight months after the event (March 2011). The primary method of data collection was in-person intercept surveys (i.e., questionnaire). Given the logistics of tracking South Africans from lower SES backgrounds for a follow-up study, many of whom have no access to computers, telephones, or permanent addresses, the decision was made to use intercept surveys in public commonly used areas. This data collection process inevitably resulted in a trend study approach whereby two demographically similar samples are used instead of the same participants across time. This method has been used successfully in similar studies such as resident perceptions of the 2008 Beijing Olympic Games (Gursoy et al. 2011).

A trained team of 28 student fieldworkers and five field coordinators from a major university in Pretoria administered the questionnaires at several high traffic public areas (e.g., shopping centers, popular squares, business districts). Site selections were designed to approximate a representative sample of the population (by demographics) in each host location. Fieldworkers worked to ensure that all entries and exits at the respective sampling location were monitored. At each location, residents were randomly intercepted and asked to complete a questionnaire. Every fifth person (or group) was targeted at the designated site; one adult from each party was identified (i.e., alternating male and female), and asked to participate in the research. Potential respondents were screened to assess if they were a resident of the respective city. Only residents completed the questionnaire, which took approximately 20 minutes to complete. In the event a resident was not able to read or write, the interviewee completed the questionnaire by reading the questions to the respondent. After eliminating questionnaires with incomplete responses and missing data points for the preevent sample,n = 1,759 questionnaires were retained for further analysis (Johannesburg, n = 373; Nelspruit, n = 357; Polokwane, n = 315; Pretoria, n = 349; Rustenburg, n = 365). For the post-event sample, n = 2,030 questionnaires were retained for further analysis (Johannesburg, n = 440; Nelspruit, n = 282; Polokwane, n = 362; Pretoria, n = 529; Rustenburg, n = 417).

Measurement and Data Analysis

The items used for this study were part of a larger research project surrounding the event. Twenty-two impact questions related to seven event impact categories were adapted from the literature (Fredline, Jago, and Deery 2003; Gursoy and Rutherford 2004; Kim and Petrick 2005; Leopkey 2009; Preuss 2007), the country’s social and political profile, and from a review of the items by a panel of faculty experts on sport event tourism from a university in the United States and South Africa. These categories were named as (1) economic, (2) tourism, (3) political, (4) psychological, (5) infrastructure, (6) opportunity and social costs, and (7) social benefits. Items were worded as statements asking respondents to agree or disagree on a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree). Overall satisfaction with QOL was measured on a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree) with one statement (“Overall, taking everything into account, I am very satisfied with my quality of life”) (Perdue, Long, and Kang 1999). This overall assessment for satisfaction with QOL was adopted because of high correlation of the subdimensions of items used in the QOL scales, and also an evaluation of the literature (Pavot and Diener 1993; Priebe et al. 1999). Another reason was the lack of space in the questionnaire that limited the use of multidimensional QOL scales. The dependent variable (i.e., event support) was measured with one item (“Overall, I support the hosting of the World Cup in South Africa”), anchored similarly to the question on satisfaction with overall QOL. Table 1 provides the means and standard deviations of all the variables included in the model. In order to test whether there were differences before and after the event in the perceived impacts, QOL and event support, a multivariate analysis of covariance (MANCOVA) was estimated with income as the covariate and a path analysis model was estimated for each sample in order to test hypotheses 1 to 8. The detailed results are presented in the pertinent section while the following section describes the multivariate analysis of covariance steps and variable preparation process for path analysis.

Table 1.

Estimated Marginal Means, Standard Errors (SE), Cronbach’s Alpha Coefficients and MANCOVA Univariate F Statistics for Items Used for the Evaluation of Impacts among 2010 World Cup Host City Residents before and after the Event.

Impacts from World Cup	M _pre	SE	M _post	SE	Univariate F _pre-post	Univariate F _income
Economic (α_pre = .66, α_post = .73)
Increased employment opportunities	4.09	.039	3.82	.062	13.755*	2.545
Increased opportunities for small business to grow	4.14	.035	3.98	.055	6.158*	7.356*
Increased the prices of goods and services	4.22	.035	4.10	.055	3.876*	5.876*
Tourism (α_pre = .78, α_post = .80)
Enhanced South Africa’s image worldwide	4.42	.026	4.66	.04111	24.896*	5.761*
Increased tourism business in the future	4.45	.027	4.33	.043	5.201*	0.784
Improved the tourism infrastructure (e.g., hotels, attractions)	4.54	.026	4.33	.041	18.314*	0.845
Political (α_pre = .67, α_post = .64)
Contributed to progress on social issues in South Africa	4.18	.033	4.12	.052	0.988	2.766
Reduced racial segregation	3.46	.042	3.43	.066	0.150	1.753
Helped with the recognition of South Africa’s political system worldwide	3.90	.034	4.23	.054	27.295*	0.010
Psychological (α_pre = .82, α_post = .87)
Increased community spirit and pride	4.23	.029	4.37	.046	6.695*	0.719
Increased feelings of national pride and patriotism	4.18	.030	4.41	.047	17.377*	0.491
Made people feel good about themselves and their community	4.25	.029	4.43	.045	10.961*	0.494
Helped bring people together in celebration	4.37	.028	4.47	.044	4.179*	2.583
Infrastructure (α_pre = .61, α_post = .53) (only one item retained from this factor)
Improved infrastructure in the country (e.g., roads)	4.37	.030	4.28	.047	2.141	0.658
Created opportunities for better housing options	3.87	.040	3.64	.063	8.921*	7.764*
Created stadiums that will be used for sports activities following the World Cup	4.33	.030	4.61	.048	23.550*	4.633*
Opportunity and social costs (α_pre = .68, α_post = .78)
Took money away from other projects that would have benefited local communities	3.47	.043	3.35	.068	1.965	4.788*
Wasted taxpayers’ money for the construction of facilities for visitors	3.22	.045	3.19	.071	0.130	0.325
Increased the crime rate	3.48	.045	3.22	.071	9.378*	0.464
Social benefits (α_pre = .72, α_post = .77)
Developed future opportunities for sport participation for all people	4.19	.031	4.18	.048	0.033	0.712
Provided once-in-a-lifetime experience for the local residents	4.28	.030	4.40	.047	4.491*	0.489
Helped people gain new skills and knowledge	4.18	.034	4.01	.054	6.475*	0.779
Quality of life
Overall, taking everything into account, I am very satisfied with my quality of life	5.12	.054	5.22	.085	0.88	0.117
Support
Overall I support(ed) the hosting of the World Cup in South Africa	6.16	.039	6.39	.061	9.925*	0.760

Note: Impacts were measured on a 5-point scale where 1 = strongly disagree, and support and satisfaction with life were measured on a 7-point scale where 1 = strongly disagree. MANCOVA = multivariate analysis of covariance.

p < .05.

Analysis Steps and Variable Preparation for Path Analysis

The first step in the analysis involved the estimation of a MANCOVA model that tested perceptual differences before and after the event regarding the event impacts, overall satisfaction with QOL, and event support. The second step involved the preparation of variables for the path analysis model. Factor analyses were conducted using principal components extraction and varimax rotation to test the unidimensionality of the items comprising each impact category. The results revealed the unidimensionality of each impact category. Subsequently, Cronbach’s alpha reliability coefficients were estimated for the pre- and postevent conditions that ranged from .63 to .80. According to the alpha coefficients, one problematic factor was identified (i.e., infrastructure), which fell below the suggested cut-off point of .70 (Lance, Butts, and Michels 2006). A decision was made to keep only one item from the infrastructure factor because of its face validity to general infrastructure (i.e., improved infrastructure in the country). From the factor and reliability analyses, and given the high correlation between factor items, a decision to use path analysis was made. The use of path analysis is recommended when the psychometric characteristics of the latent factors support the estimation of a mean score for the variables (Landis, Edwards, and Cortina 2009). Based on the factor analyses and the acceptable range of the Cronbach’s alpha values, seven event impact variables were created through the estimation of means scores from the items comprising each factor in both pre- and postevent samples (one being the single infrastructure item) to use in a path analysis.

The third step was the estimation of path models to test the differential weight of impacts on QOL and whether overall satisfaction with QOL mediated the relationship between event impacts and event support. To estimate the models for the pre- and the post-event samples, EQS 6.1 software was used. The normed fit index (NFI), comparative fit index (CFI), and the standardized root mean square residual (SRMR) were used to estimate model fit (Hu and Bentler 1998). The assumptions of multivariate normality through the examination of Mardia’s coefficient were not met. Thus, EQS’s option to correct for nonnormality by estimating model parameters using robust statistics such as the Satorra Bentler chi-square (Chou, Bentler, and Satorra 1991) was used to assess the model fit and account for this normality departure.

Results

In this section, the description of samples is presented and compared with the South African census, followed by the MANCOVA and path analysis results.

Description of Samples

The two samples from the preevent and the postevent stage were demographically similar. Chi-square tests for gender, education, and race across the two samples showed statistical differences between the two samples only in education (χ² = 26.866, p < .001). A t-test on income and age showed a statistical difference between the two samples on income only (t = −3.327, p < .01), with the postevent sample having a higher average income than the preevent sample. For the preevent sample, 57.6% were male, the race classification for the majority was black (83.8%), while the education level for the largest percentage in the sample (38.8%) was secondary followed by 26.7% having a diploma, 15.5% a certificate, 11.4% a degree, 4.4% primary school education, and 3.2% an honors degree. The average age was 29.84 (SD = 8.80) and the average income was 96,604 rands¹ (SD = 209,466). For the postevent sample, 54.6% were male, 82.3% were black, 34.3% had secondary education, followed by 31.8% who had a diploma, 14.6% a certificate, 14.1% a degree, and 2.4% an honors degree. The average age was 30.19 (SD = 9.34), and the average income was 132,774 rands (SD = 138,703).

The results from the South African census (Statistics South Africa 2012) show that males comprise 48.5% of the population. In terms of education, 4.16% of the population more than 20 years old have a diploma, followed by 2.28% who have a bachelor’s degree, and 1.42% who have a certificate. Census income was reported in terms of income brackets. In 2011, the highest percentage of South Africans (16.9%) earned between R9,601 and R14,400, followed by 16.4% who earned between R4,800 and R9,600. The race classification from the census results revealed that 79.6% of the South African population is black, followed by 8.99% colored and 8.76% white, while Asians comprised 2.63%. In terms of age, 20.7% of the population is between 15 and 24 years old, 16% is between 25 and 34 years old, 11.1% is between 35 and 44 years old, 8.5% between 45 and 54 years old, and 11.1% is more than 55 years old.

Upon comparison of the census results with the sample descriptions, it appears that the sample respondents were fairly representative of the population in terms of gender, race, and age but not for income and education. Clearly, the respondents have higher income and education compared to the general South African population. Perhaps this is justifiable, as the method of data collection [intercepts of residents were mainly in urban (and not rural) commercial and other sites where people went to shop] created an overrepresentation of relatively more affluent South Africans.

Comparing Impact Perceptions before and after the Event

To understand impact perception differences, satisfaction with QOL and event support before and after the event, a MANCOVA was estimated for all impact items, QOL, and event support as the dependent variable, while including income as the covariate. A decision to use income as a covariate was made since income and education can be highly correlated factors. Thus, the researchers felt that testing one of the two would account for any differential effects in the analysis. The independent variable was the pre–post group. The multivariate results showed a significant Pillai’s trace statistic (Pillai’s = .11, p < .001, partial η² = .119) for the pre–post groups and a significant Pillai’s trace for the income covariate (Pillai’s = .04, p < .001, partial η² = .046). Pillai’s trace was examined because it accounts for the violation of the Box’s test of sphericity that was present in the results for the MANCOVA test (Tabachnick and Fidell 2007). On examination of the univariate results, the income covariate was significant only in 6 of the 22 impact items. The comparison of perceptions before and after the event revealed differences in 16 of the 22 impact items. There was no clear pattern of increase among the item scores as seven impact item scores significantly decreased after the event, eight items increased, and six items were not statistically different. However, a clear pattern was found with the scores for the psychological impact items, which increased postevent while those for the economic impact items clearly decreased. These results are presented in Table 1 along with descriptive statistics (estimated marginal means and standard errors) of the variables in the study.

Model Results

The fit indices for the preevent sample and the postevent sample were very similar. For preevent, the indices were χ² = 231.64, df = 7, NNFI = .93, CFI = .93, SRMR = .08. For postevent, the indices were χ² = 324.18, df = 7, NNFI = .93, CFI = .93, SRMR = .08. The standardized path estimates results show slight differentiations in terms of the impact types that influenced QOL and event support before and after the event. In the preevent condition, significant predictors of overall satisfaction with QOL included political impacts (β = .11, p < .05), psychological impacts (β = .12,p < .05), and social benefits (β = .16, p < .05). As well, overall satisfaction with QOL was a significant predictor of event support (β = .22, p < .05). Collectively, these results lend support for hypotheses 3, 4, 7, and 8. In the postevent condition, residents appreciated the economic impacts of the event (β = .09, p < .05), along with the political impacts (β = .14, p < .05), psychological impacts (β = .10, p < .05), and social benefits (β = .09, p < .05). Overall satisfaction with QOL was also a significant predictor of event support (β = .22, p < .05). Taken together, these results support hypotheses 1, 3, 4, 7, and 8.

To test our mediation hypothesis, the indirect effects for the impact variables (i.e., on event support) were examined. In the preevent condition, significant indirect effects for political (β = .02, p < .05), psychological (β = .02, p < .05), and overall benefits (β = .03, p < .05) were found, which suggests partial mediation for QOL. For the postevent condition, significant indirect effects for economic (β = .01, p < .05), political (β = .02, p < .05), psychological (β = .02, p < .05), and social benefits (β = .01, p < .05) were found, which also suggests a partial mediation effect of QOL. Irrespective of the significant (albeit small) path estimates, the results suggest the important role of overall satisfaction with QOL as the exchange platform variable in the pre- and postevent models for some of the impact categories. Table 2 illustrates the path coefficients from the impact factors to overall satisfaction with QOL and from QOL to event support. Table 3 presents the means, standard deviations and correlations for the pre- and post-event model variables.

Table 2.

Path Analysis Results from Pre- and Postevent Stages for Impacts, Quality of Life, and Support.

	Preevent		Postevent
Hypotheses	Standardized Beta	R ²	Standardized Beta	R ²
Hypothesis 1. Economic → Quality of life	.06		.09***
Hypothesis 2. Tourism → Quality of life	−.06		−.007
Hypothesis 3. Political → Quality of life	.11***		.14***
Hypothesis 4. Psychological → Quality of life	.12***		.10**
Hypothesis 5. Infrastructure → Quality of life	.002		.02
Hypothesis 6. Opportunity and social costs → Quality of life	.02		−.02
Hypothesis 7. Social benefits → Quality of life	.16***		.09**
Hypothesis 8. Quality of life → Support for World Cup hosting	.22***		.18***
R ² (for quality of life)		.10		.12
R ² (for event support)		.05		.035

p < .01, ***p < .001.

Table 3.

Correlations, Means, and Standard Deviations for Pre- and Postevent Samples for the Path Models Tested in the Study.

Preevent	1	2	3	4	5	6	7	8	9
1. Support	1
2. Quality of life	.22	1
3. Infrastructure	.03	.16	1
4. Economic	.03	.16	.32	1
5. Tourism	.03	.14	.48		1
6. Political	.05	.24	.31	.53		1
7. Psychological	.05	.26	.58	.39	.42	.54	1
8. Opportunity and social costs	.01	.05	−.01	.14	.51	.04	.003	1
9. Social benefits	.06	.27	.44	.32	.42	.43	.55	.10	1
M	6.15	5.12	4.38	4.14	3.46	3.82	4.25	3.40	4.22
SD	1.32	1.69	0.91	0.88	0.71	0.92	0.76	1.12	0.80

Postevent	1	2	3	4	5	6	7	8	9

1. Support	1
2. Quality of life	.18	1
3. Infrastructure	.04	.2	1
4. Economic	.04	.22	.28	1
5. Tourism	.04	.23	.52	.48	1
6. Political	.05	.29	.51	.50	.57	1
7. Psychological	.05	.27	.63	.29	.53	.58	1
8. Opportunity and social costs	−.007	−.03	−.11	−.004	−.06	−.02	−.08	1
9. Social benefits	.04	.23	.46	.23	.49	.40	.50	.006	1
M	6.19	5.29	4.34	3.85	4.42	3.94	4.38	3.23	4.18
SD	1.12	1.66	0.96	1.01	0.76	0.83	0.73	1.14	0.81

Discussion

The purpose of this study was to (1) compare if perceived impacts, satisfaction with QOL, and event support differ before and after the event, and (2) to extend previous research by examining the mediating role between QOL, event impacts (e.g., economic, tourism, infrastructure, political, psychological, opportunity and social costs and social benefits), and residents’ support for hosting mega sport events. There are three main theoretical contributions that this study offers to the mega-event and host–resident literature: (1) the salience of political, psychological, and social benefits perceived to improve QOL among residents in the host nation; (2) a dynamic exchange process, where residents evaluate differently the impacts of a mega-event for their QOL before and after the event along with the varying predictive power of certain impacts postevent; and (3) the overall QOL perceptions as the exchange platform for some impact types (benefits acquired) that create support of residents for the mega-event in their country.

Based on the specific impact comparison before and after the event, the increase in psychological, destination image enhancement, and sport infrastructure improvements is evident. In contrast, the economic impacts had a significant postevent decrease, a result that may suggest the disconfirmation of expectations regarding economic benefits from the World Cup. The strength of intangible impacts, however, may contribute more to the notion of “worth it” (Hiller and Wanner 2011) when it comes to mega-event hosting. This study showed that increased support toward the event was evident after its completion. Surprisingly, there were no statistical differences in changes in QOL before and after the event although there was a slight increase in the mean score after the event, which may indicate a slight improvement. This finding is further substantiated by the results from the path analysis models where the exact influence of the various impacts on the QOL concept is discussed.

The specific findings from the path models indicate slight differences in the models based on the number of significant β coefficients between QOL and impacts. More specifically, the two models (i.e., pre- and postevent) differed in terms of the influence of perceived economic impacts on QOL. Perceived economic impact (postevent) had a positive effect on the residents’ QOL, while in the preevent stage it was not a significant predictor. This can be partially explained by results from prior work where mega-event hosting produced economic impacts on job growth, tourist attractions, tourist spending, tax revenues, and infrastructure development (e.g., Chalip 2002; Gursoy and Kendall 2006; Owen 2005). Furthermore, the literature has noted that economic impacts significantly influence overall QOL (Cummins 1997; Diener and Suh 1997). As residents feel increasingly satisfied with their personal and community economic situation (e.g., income, standard of living, housing, and personal possessions), they tend to be happier and more satisfied with their QOL (Cummins et al. 2003; Cummins 1997). The post-event manifestation of this result suggests two ideas: (1) it either takes time to evaluate economic impacts prior to the event or (2) anxiety might exist preevent with costs (Kim, Gursoy, and Lee 2006).

Residents perceived that the political impacts experienced from hosting the World Cup had a positive effect on their QOL both pre- and postevent. This result is important for a developing nation like South Africa, where political turbulence has been part of the societal grid (Cornelissen and Swart 2006). Political system recognition, the reduction of racial segregation and progress in social issues in South Africa improved the satisfaction with QOL and provided the local residents with a global platform for such recognition. This later statement is also supported by the significant postevent increase of residents’ perceptions regarding the global recognition of South Africa’s political system.

The importance of intangible impacts was also evidenced in the increased mean scores of the psychological impacts post-event and its positive impact on QOL underlining the importance of emotional investment through the experience of excitement and pride, a process coined as psychic income (Burgan and Mules 1992) associated with hosting mega events (Ritchie 1984; Waitt 2003). Psychological benefits have discounting effects as found in Gursoy and Kendall’s (2006) study, where increased pride created higher tolerance among residents regarding negative economic effects. As researchers have started to examine event-related impacts over time, several studies have shown that psychological benefits are frequently higher after the event, and may be rated as the most important benefit (Kaplanidou 2012; Kim and Petrick 2005; Kim, Gursoy, and Lee 2006). Perhaps, the results of this study suggest that social and psychological benefits of hosting mega-events and their contribution to QOL coupled with their significant impact on satisfaction with QOL in both pre- and postevent samples are sustained over time. This contention appears to be supported by Karadakis and Kaplanidou (2012) in their study of Vancouver residents in relation to the 2010 Winter Olympic Games and by Kaplanidou (2012), who examined impact perceptions of Olympic Games host city residents in year 2010, which was 2, 6, 10, and fourteen years after the event concluded in the pertinent Olympic host cities. For example, the residents of Atlanta in the Kaplanidou (2012) study indicated pride as one important legacy of the Games for the QOL while for Sydney residents, global awareness of Australian culture and pride were also noted as important. For Athens and Beijing residents (more recent hosts), more tangible sport and general infrastructure impacts were important, which is similar with the perceptions of South African residents given the short postevent time the data were collected from them.

As hypothesized, the data supported the direct positive relationship between social benefits and QOL. Although not empirically tested, previous research has suggested that the benefits associated with hosting a mega-event can improve residents’ QOL (Deccio and Baloglu 2002). The results of the study reinforce that residents who appreciated the importance of social benefits for their QOL from hosting a mega-event tend to be more supportive (Deccio and Baloglu 2002; Getz 1997; Gursoy and Kendall 2006; Gursoy and Rutherford 2004). Perhaps Social Exchange Theory illuminates these results. As mentioned, if residents experience a benefit from engaging in an interaction (i.e., in the case of the current study, improvement in QOL is perceived as a benefit from hosting the FIFA World Cup), then residents should be supportive and continue to engage in future interactions (Fredline 2005). Therefore, as the results suggest, with South Africa having hosted the 2010 World Cup, residents perceived positive outcomes and social benefits that resulted in improvement in their QOL. This, in turn, resulted in residents’ support for the event, which should transfer to support for future mega-events (Cornelissen and Swart 2006).

The lack of support for hypothesis 6 (costs) can also be explained by the positive and significant relationship between perceived benefits and QOL. Previous research has found that host communities downplayed hosting costs if they expected positive benefits to accrue (Kim, Gursoy, and Lee 2006). The results of this study are consistent with prior work, which has documented insignificant relationships between resident support and hosting costs (Deccio and Baloglu 2002; Gursoy and Kendall 2006). When transposing this discussion to Social Exchange Theory, it is apparent that residents perceived to have experienced more benefits than costs. Consequently, they did not perceive that their QOL was negatively affected. Finally, as Gursoy and Kendall (2006) discussed, “perceptions of impacts are not mutually exclusive; a change in perceptions of one type is likely to influence the perceptions of other types of impacts. This suggests that if residents place more importance on benefits, they may overlook the costs associated with hosting the event” (p. 616).

Overall satisfaction with QOL was found to have a positive relationship on support for hosting the World Cup. These results are consistent with previous studies utilizing Social Exchange Theory to predict whether economic, political, psychological, and social benefits influenced resident support (Bull and Lovell 2007; Deccio and Baloglu 2002; Fredline 2005; Gursoy and Kendall 2006; Gursoy and Rutherford 2004; Kim, Gursoy, and Lee 2006; Preuss and Solberg 2006; Yoon, Gursoy, and Chen 2001). The meaning of this result, however, lies in the importance of QOL as a target outcome for host cities and host organizing committees, if they want to achieve extensive collaboration of a key stakeholder group in event organization: the residents. Surprisingly, respondents did not perceive that infrastructure had a significant impact on their QOL or their support for the event. Although hypothesis 2 was not supported, there was a negative relationship between tourism impacts and QOL. A possible explanation for this can be attributed to residents experiencing negative impacts such as capacity constraints, displacement, and physical removal of residents to allow for event-related infrastructure, crowding that causes local residents difficulty to access resources or the event itself, disagreements between residents and visitors, and the disruption of residents’ daily lives (Gursoy and Kendall 2006; Jones 2001; Tosun 2002). Another potential explanation may be found in Doxey’s Irridex model (1975). The model suggests that as a community develops to fulfill tourism objectives, resident perceptions toward the community changes from a supportive euphoria to a negative or nonsupportive attitude toward tourism (Vargas-Sanchez, Porras-Bueno, and Plaza-Mejıa 2011).

Media attention or event publicity may also explain the lack of significant relationships between tourism and infrastructure impacts, costs, and support. The anticipation created by the national media, government agencies, and the organizing committee may influence residents’ perceptions with regards to expecting positive impacts from hosting a mega-event that may exceed the costs (Kim, Gursoy, and Lee 2006). Before the event, the information offered by the media and general supporters of the event may persuade residents about the event’s positive impacts, which can serve as a point of reference in residents’ memory (Kim, Gursoy, and Lee 2006). Furthermore, the findings in the current study run in concert with Chien et al. (2011), who identified that an event covered by the media can be a useful method of promotion for a community. Yet a negative representation of the event can decrease and even reverse the promotion effect. Specifically, Chien et al. (2011) found that greater support for the event was provided by residents when the event was portrayed positively. If proper communication strategies from the host organization and community encourage media to portray a successful hosting of the mega-event, then a more positive awareness can be created, which can enhance residents’ perceptions of the impacts of these events. Residents would then feel the event contributes to the improvement of their QOL (e.g., awareness about infrastructure improvement, more sport opportunities) and, therefore, be more supportive.

Practical Implications

Given the significant differences before and after the event, and the significant influence of overall satisfaction with QOL on event support and the differential influence of impacts on satisfaction with QOL, there are a number of practical recommendations that should be considered. First, event impact perceptions change with time. Thus, each host nation should communicate positive changes the event brings on a regular basis to garner higher support and a successful event-hosting environment. Before the event, lower economic expectations should be communicated and the cultivation of sociopsychological benefits that arise from hosting expectations should be a higher priority goal for the host organizing committee and city. Following the event, communication of economic benefits along with sociopsychological benefits should occur through central governmental efforts throughout the host country. This would allow for a coordinated, more positive, and sustainable impression of event hosting. These strategies will contribute to higher satisfaction of QOL, which is a benefit to the local people. In other words, interventions designed to improve the host community’s living conditions are a positive change in any society. These strategies can also garner continuous support toward mega-event hosting from one of the key stakeholders in mega-event hosting, the local residents. For South Africa, this is critical given their continuous interest to bid for another global mega-event, that is, the Summer Olympic Games.

Another implication is that QOL is considered as an exchange for the inconvenience locals experience when the nation prepares for the event. Social benefits are of critical importance for QOL improvement. As a result, developing participation opportunities for locals, and taking advantage of the event to increase knowledge and skills, can be target outcomes for host organizations and communities. To achieve these benefits, collaborative efforts among sport and community organizations will likely be necessary. This collaboration effort can be a planning step toward any future bid for host countries as well.

Finally, for future host cities, the results suggest that intangible impacts can relate more to people’s satisfaction with QOL and event support. Thus, the need to embed the importance of intangible impacts in strategic planning during event preparation, hosting, and postevent stages is obvious. The importance of intangible impacts can support the rationale for staging mega-events. Although South Africa experienced large infrastructure changes (e.g., new stadiums, highways), there was no influence of that variable on satisfaction with QOL and event support. This key finding should be considered within the context of QOL and infrastructure measurement limitations. Also, the present study and its findings should offer a useful case study for developing nations that are future hosts to mega-events (e.g., Brazil will host the 2014 World Cup and the 2016 Olympic Games).

Limitations and Future Research

As with every research project, there are limitations to consider. The sample sizes were large, which could inflate Type I errors. However, the reported p values allow the reader to examine the level of significance despite the impact of large samples. Second, the samples in this study do not fully represent the South African population (rural and urban residents) in terms of income and education, which may create bias for the relationship between impacts and the dependent variables of event support and QOL. Finally, the concept of satisfaction with QOL was operationalized with only one item because of resource limitations. Although one overall evaluation item is a good correlate of QOL subdimensions, it may still limit the findings in terms of relationships of impacts with specific subdomains. Future research could address this relationship with a multidimensional QOL scale.

This research extends the theoretical models proposed in the literature about residents’ support for tourism development by adding QOL as a mediator. This study also reveals that perceived political impacts created by the FIFA World Cup have a larger contribution on perceived QOL of South African residents than other event-related impacts, although economic and psychological impacts were also important after the event. Although the models were statistically acceptable, the variance explained by the variables were relatively low, which raises more questions than answers. Perhaps other variables should be considered in future research such as residents’ overall attitudes toward the event (Andereck and Vogt 2000; Prayag et al., forthcoming), economic dependency that results from hosting an event (Perdue, Long, and Kang 1999), as well as overall perceptions of successful event hosting. Future research could also involve a longitudinal study (panel data) where the change and influence of impacts among the same sample people is documented.

Footnotes

Acknowledgements

We would like to thank Tshwane University of Technology (TUT), Pretoria, South Africa, for their unconditional financial and human resources support for the completion of this project. We are truly grateful. We would like to specially thank Dr. Prins Nevhutalu, Deputy Vice-Chancellor of Research, Innovation and Partnership at TUT, for his vision and commitment to this project. This project would not have materialized without the dedicated efforts of numerous TUT students and other assistants, to whom we are indebted for their time and efforts.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported in kind by the Tshwane University of Technology, Pretoria, SA. The University covered the costs for surveys and human resources needed to conduct the research. No direct compensation was received for this project.

Notes

Author Biographies

Kyriaki (Kiki) Kaplanidou is an assistant professor in the Department of Tourism, Recreation and Sport Management and affiliate member of the Eric Friedheim Tourism Institute. Her research focuses on sport tourist behaviors and the ways mega and small scale sport events impact host communities.

Kostas Karadakis is an Assistant Professor of Sport Management in the School of Business in Southern New Hampshire University. His research interests include sport event impacts and their influence on quality of life, sport tourism, legacy management and community development through sport.

Heather Gibson is Professor at the Department of Tourism, Recreation and Sport Management and Associate Director of the Eric Friedheim Tourism Institute. Her research interests include sport tourism with a focus on sport-related travel in later life and small-scale events; leisure and tourism in later life; women travelers; and perceived risk in travel.

Brijesh Thapa, PhD, is the Director of the Eric Friedheim Tourism Institute at the University of Florida. He is also an Honorary Professor in the Department of Tourism Management at Tshwane University of Technology in South Africa. Overall, his research theme is within the nexus of tourism, conservation and sustainability.

Matthew Walker is an Assistant Professor in the College of Health and Human Performance at the University of Southern Mississippi. His primary research interests lie in Organizational Theory, but more specifically in the areas of social and environmental responsibility.

Sue Geldenhuys is the Head of Department Tourism Management at Tshwane University of Technology in South Africa. She has published in accredited as well as in other tourism journals and have authored a number of chapters in books published by international as well as local Publishers related to tourism.

Willie Coetzee has more than 13 years teaching experience at University-level in South Africa. His research focuses on sustainable tourism, tourism development, tourism Research and Project Management.

References

Alegi

(2008). “‘A Nation to Be Reckoned with’: The Politics of World Cup Stadium Construction in Cape Town and Durban, South Africa.” African Studies, 67 (3): 397-422.

Andereck

K. L.

Nyaupane

G. P.

(2011). “Exploring the Nature of Tourism and Quality of Life Perceptions among Residents.” Journal of Travel Research, 50 (3): 248-60.

Andereck

K. L.

Valentine

K. M.

Knopf

R. C.

Vogt

C. A.

(2005). “Residents” Perceptions of Community Tourism Impacts.” Annals of Tourism Research, 32 (4): 1056-76.

Andereck

K. L.

Valentine

Vogt

Knopf

(2007). “A Cross-cultural Analysis of Tourism and Quality of Life Perceptions.” Journal of Sustainable Tourism, 15 (5): 483-500.

Crompton

(1993). “Residents’ Strategies for Responding to Tourism Impacts.” Journal of Travel Research, 32 (1): 47-50.

Bob

Swart

(2009). “Resident Perceptions of the 2010 FIFA Soccer World Cup Stadia Development in Cape Town.” Urban Forum, 20 (1): 47-59.

Bull

Lovell

(2007). “The Impact of Hosting Major Sporting Events on Local Residents: An Analysis of the Views and Perceptions of Canterbury Residents in Relation to the Tour de France 2007.” Journal of Sport & Tourism, 12 (3–4): 229-48.

Burgan

Mules

(1992). “Economic Impact of Sporting Events.” Annals of Tourism Research, 19 (4): 700-10.

Chalip

(2002). “Using the Olympics to Optimise Tourism Benefits: University Lecture on the Olympics” [online article]. Barcelona: Centres d’ Estudis Olimpics (UAB), International Chair in Olympism (IOC-UAB). http://olympicstudies.uab.es/lectures/web/pdf/chalip.pdf.

10.

Chalip

(2006). “Towards Social Leverage of Sport Events.” Journal of Sport & Tourism, 11 (2): 109-27.

11.

Chhabra

Gursoy

(2009). “Life Dynamism Explorations on Perceived Quality of Life and Social Exchange Paradigms in Casino Settings.” Leisure Sciences, 31 (2): 136-57.

12.

Chien

P. M.

Ritchie

Shipway

Henderson

(2011). “I Am Having a Dilemma: Factors Affecting Resident Support of Event Development in the Community.” Journal of Travel Research, 51 (4): 451-63.

13.

Chou

C.-P.

Bentler

P. M.

Satorra

(1991). “Scaled Test Statistics and Robust Standard Errors for Non-normal Data in Covariance Structure Analysis: A Monte Carlo Study.” British Journal of Mathematical and Statistical Psychology, 44 (2): 347-57.

14.

Collins

Calvin

Munday

(2009). “Assessing the Environmental Impacts of Mega Sporting Events: Two Options?” Tourism Management, 30 (6): 828-37.

15.

Cornelissen

Swart

(2006). “The 2010 Football World Cup as a Political Construct: The Challenge of Making Good on an African Promise.” Sociological Review, 54: 108-23.

16.

Cummins

(1997). “The Domain of Life Satisfaction: An Attempt to Order Chaos.” Social Indicators Research, 38: 303-28.

17.

Cummins

Eckersley

Pallant

Vugt

J. V.

Misajon

(2003). “Developing a National Index of Subjective Wellbeing: The Australian Unity Wellbeing Index.” Social Indicators Research, 64: 159-90.

18.

Deccio

Baloglu

(2002). “Non-host Community Resident Reactions to the 2002 Winter Games: The Spillover Impacts.” Journal of Travel Research, 41 (1): 46-56.

19.

Diener

(2000). “Subjective Well-Being: The Science of Happiness and a Proposal for a National Index.” American Psychologist, 55: 34-43.

20.

Diener

Suh

(1997). “Measuring Quality of Life: Economic, Social, and Subjective Indicators.” Social Indicators Research, 40: 189-216.

21.

Doxey

G. V.

(1975). “A Causation Theory of Visitor-Resident Irritants: Methodology and Research Inferences.” Proceedings of the 6th Annual Conference of the Travel Research Association. San Diego, CA: Travel and Tourism Research Association, pp. 195-98.

22.

Emerson

R. M.

(1976). “Social Exchange Theory.” Annual Review of Sociology, 2: 335-62.

23.

Fredline

(2005). “Host and Guest Relations and Sport Tourism.” Sport in Society, 8 (2): 263-79.

24.

Fredline

Jago

Deery

(2003). “The Development of a Generic Scale to Measure the Social Impacts of Events.” Event Management, 8: 23-37.

25.

Getz

(1997). Event Management and Event Tourism. New York: Cognizant Communication.

26.

Gratton

Preuss

(2008). “Maximizing Olympic Impacts by Building up Legacies.” International Journal of the History of Sport, 25: 1922-38.

27.

Gursoy

Chi

Chen

(2011). “Temporal Change in Resident Perceptions of a Mega-Event: The Beijing Olympic Games.” Tourism Geographies, 13: 2999-324.

28.

Gursoy

Kendall

K. W.

(2006). “Hosting Mega Events: Modeling Locals’ Support.” Annals of Tourism Research, 33 (3): 603-23.

29.

Gursoy

Rutherford

D. G.

(2004). “Host Attitudes toward Tourism: An Improved Structural Model.” Annals of Tourism Research, 31 (3): 495-516.

30.

Hagerty

M. R.

Cummins

R. A.

Ferriss

A. L.

Land

Michalos

A. C.

Peterson

Sharpe

Sirgy

Vogel

(2001). “Quality of Life Indexes for National Policy: Review and Agenda for Research.” Social Indicators Research, 55 (1): 1-96.

31.

Haralambopoulos

Pizam

(1996). “Perceived Impacts of Tourism: The Case of Samos.” Annals of Tourism Research, 23: 503-26.

32.

Hiller

H. H.

(2000). “Mega-Events, Urban Boosterism and Growth Strategies: An Analysis of the Objectives and Legitimations of the Cape Town 2004 Olympic Bid.” International Journal of Urban and Regional Research, 24 (2): 449-58.

33.

Hiller

Harry H.

Wanner

Richard A.

(2011). “Public Opinion in Host Olympic Cities: The Case of the 2010 Vancouver Winter Games.” Sociology, 45 (5): 883-99.

34.

Hritz

Ross

(2010). “The Perceived Impacts of Sport Tourism: An Urban Host Community Perspective.” Journal of Sport Management, 24: 119-38.

35.

Bentler

P. M.

(1998). “Fit Indices in Covariance Structural Equation Modeling: Sensitivity to Underparameterized Model Misspecification.” Psychological Methods, 3: 424-53.

36.

Jin

Zhang

Connaughton

(2011). “Residents’ Perceptions of Environmental Impacts of the 2008 Beijing Green Olympic Games.” European Sport Management Quarterly, 11 (3): 275-300.

37.

Jones

(2001). “Mega-Events and Host-Region Impacts: Determining the True Worth of the 1999 Rugby World Cup.” International Journal of Tourism Research, 3 (3): 241-51.

38.

Kaplanidou

(2011). “Olympic Research Corner: A Question of Legacy.” Olympic Review, 79: 64-67.

39.

Kaplanidou

(2012). “The Importance of Legacy Outcomes for Olympic Games Four Summer Host Cities Residents’ Quality of Life: 1996-2008.” European Sport Management Quarterly, 12 (4): 397-433.

40.

Kaplanidou

Karadakis

(2010). “Understanding the Legacies of a Host Olympic City: The Case of the 2010 Vancouver Olympic Games.” Sport Marketing Quarterly, 19: 110-17.

41.

Karadakis

Kaplanidou

(2012). “Legacy Perceptions among Host and Non-host Olympic Games Residents: A Longitudinal Study of the 2010 Vancouver Olympic Games.” European Sport Management Quarterly, 12 (3): 243-64.

42.

Kasimati

Evangelia

. (2003). “Economic Aspects and the Summer Olympics: A Review of Related Research.” International Journal of Tourism Research, 5: 433-44.

43.

Kim

H. J.

Gursoy

Lee

S.-B.

(2006). “The Impact of the 2002 World Cup on South Korea: Comparisons of Pre- and Post-Games.” Tourism Management, 27: 86-96.

44.

Kim

S. S.

Morrison

A. M.

(2005). “Change of Images of South Korea among Foreign Tourists after the 2002 FIFA World Cup.” Tourism Management, 26 (2): 233-47.

45.

Kim

S. S.

Petrick

J. F.

(2005). “Residents’ Perceptions on Impacts of the FIFA 2002 World Cup: The Case of Seoul as a Host City.” Tourism Management, 26 (1): 25-38.

46.

Lance

C. E.

Butts

M. M.

Michels

L. C.

(2006). “The Sources of Four Commonly Reported Cutoff Criteria: What Did They Really Say?” Organizational Research Methods, 9: 202-21.

47.

Landis

Edwards

Cortina

(2009). “On the Practice of Allowing Correlated Residuals among Indicators in Structural Equation Models.” In Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in the Organizational and Social Sciences, edited by Lance

Vandenberg

New York: Routledge.

48.

Lee

C.-K.

Lee

Y.-K.

Lee

(2005). “Korea’s Destination Image Formed by the 2002 World Cup.” Annals of Tourism Research, 32 (4): 839-58.

49.

Leopkey

(2009). “2008 IOC Post graduate Grant Final Report: The Historical Evolution of Olympic Legacy.” University of Ottawa. http://doc.rero.ch/record/12537 on June 2, 2012, International Olympic Committee.

50.

Lindberg

Johnson

R. L.

(1997). “Modeling Resident Attitudes toward Tourism.” Annals of Tourism Research, 21: 402-24.

51.

Liu

J. C.

Var

(1986). “Resident Attitudes towards Tourism Impacts in Hawaii.” Annals of Tourism Research, 13: 193-214.

52.

McCartney

Thomas

Thomson

Scott

Hamilton

Hanlon

Bond

(2010). “The Health and Socioeconomic Impacts of Major Multi-sport Events: Systematic Review (1978-2008).” British Medical Journal, 340: c2369.

53.

McCool

Martin

(1994). “Community Attachment and Attitudes towards Tourism Development.” Journal of Travel Research, 32 (3): 29-34.

54.

McGehee

Andereck

K. L.

(2004). “Factors Predicting Rural Residents’ Support of Tourism.” Journal of Travel Research, 43: 131-40.

55.

Meyer

(2011). “An Exploratory Investigation of Perceived Tourism Impacts on Resident Quality of Life and Support for Tourism in Cologne, Germany.” Unpublished doctoral dissertation, University of Florida.

56.

Murphy

N. M.

Bauman

(2007). “Mass Sporting and Physical Activity Events—Are They ‘Bread and Circuses’ or Public Health Interventions to Increase population levels of physical activity?” Journal of Physical Activity and Health, 4: 193-202.

57.

Nawijn

Jeroen

Ondrej

Mitas

. (2012). “Resident Attitudes to Tourism and Their Effect on Subjective Well-Being: The Case of Palma De Mallorca.” Journal of Travel Research, 51: 531-41.

58.

Nichols

Giacopassi

Stitt

(2002). “Casinos Gambling as a Catalyst of Economic Development: Perceptions of Residents in New Casino Jurisdictions.” Tourism Economics, 8: 59-75.

59.

Olympic Studies Centre. (2003). “The Legacy of the Olympic Games: 1984-2000: Conclusions and Recommendations.” International Olympic Committee.

60.

Owen

J. G.

(2005). “Estimating the Cost and Benefit of Hosting Olympic Games: What Can Beijing Expect from Its 2008 Games?” The Industrial Geographer, 3 (1): 1-18.

61.

Osborne

(1992). “The Quality Dimension: Evaluating Quality of Service and Quality of Life in Human Services.” British Journal of Social Work, 22: 437-53.

62.

Pavot

Diener

1993. “Review of the Satisfaction with Life Scale.” Psychological Assessment, 5: 164-72.

63.

Perdue

R. R.

Long

P. T.

Kang

Y. S.

(1999). “Boomtown Tourism and Resident Quality of Life: The Marketing of Gaming to Host Community Residents.” Journal of Business Research, 44 (3): 165-77.

64.

Prayag

Hosany

Nunkoo

Alders

(Forthcoming). “London Residents’ Support for the 2012 Olympic Games: The Mediating Effect of Overall Attitude.” Tourism Management. https://dx-doi-org.web.bisu.edu.cn/10.1016/j.tourman.2012.08.003.

65.

Preuss

(2007). “FIFA World Cup 2006 and Its Legacy on Tourism.” In Trends and Issues in Global Tourism, edited by Conrady

Buck

Berlin: Springer, pp. 83-102.

66.

Preuss

Solberg

(2006). “Attracting Major Sporting Events: The Role of Local Residents.” European Sport Management Quarterly, 6 (4): 391-41.

67.

Priebe

Huxley

Knight

Evans

(1999). “Application and Results of the Manchester Short Assessment of Quality of Life (Mansa).” International Journal of Social Psychiatry, 45 (1): 7-12.

68.

Ritchie

(1984). “Assessing the Impacts of Hallmark Events: Conceptual and Research Issues.” Journal of Travel Research, 22 (1): 2-11.

69.

Rogerson

C. M.

(2009). “Mega Events and Small Enterprise Development: The 2010 FIFA World Cup Opportunities and Challenges.” Development Southern Africa, 26 (3): 335-52.

70.

Sautter

Elise Truly

Leisen

Brigit

. (1999). “Managing Stakeholders a Tourism Planning Model.” Annals of Tourism Research, 26 (2): 312-28.

71.

Schalock

R. L.

Brown

Cummins

Felce

Matikka

Keith

Parmenter

(2002). “Conceptualization, Measurement, and Application of Quality of Life for Persons with Intellectual Disabilities: Report of an International Panel of Experts.” Mental Retardation, 40: 457-70.

72.

Solberg

H. A.

Preuss

(2007). “Major Sport Events and Long-Term Tourism Impacts.” Journal of Sport Management, 21 (2): 213-34.

73.

Statistics South Africa. (2012). General Household Survey. http://www.statssa.gov.za/publications/P0318/P0318April2012.pdf.

74.

Swart

Bob

(2007). “The Eluding Link: Toward Developing a National Sport Tourism Strategy in South Africa beyond 2010.” Politikon, 34: 373-91.

75.

Tabachnick

Barbara G.

Fidell

Linda S.

(2007). Using Multivariate Statistics. 5th edition. Boston, MA: Pearson Education.

76.

Theron

(2008). “The Unification Goal in South Africa and Africa: The Ideological Challenge in Hosting the 2010 World Cup.” World Journal of Managing Events, 2 (1): 56-63.

77.

Toohey

Veal

A. J.

(2007). The Olympic Games: A Social Science Perspective. Cambridge, MA: CABI.

78.

Tosun

(2002). “Host Perceptions of Impacts: A Comparative Tourism Study.” Annals of Tourism Research, 29 (1): 231-45.

79.

Twynam

D. G.

Johnston

(2004). “Changes in Host Community Reactions to a Special Sporting Event.” Current Issues in Tourism, 7 (3): 242-61.

80.

Van Wyk

(2008). “The 2010 FIFA World Cup in South Africa: The Politics of Hosting a Mega International Event.” World Journal of Managing Events, 2 (1): 1-9.

81.

Vargas-Sanchez

Porras-Bueno

Plaza-Mejıa

(2011). “Explaining Residents’ Attitudes towards Tourism: Is a Universal Model Possible?” Annals of Tourism Research, 38 (2): 460-80.

82.

Walker

Kaplanidou

Gibson

Thapa

Geldenhuys

Coetzee

(2012). “Win in Africa, With Africa”: Social Responsibility, Event Image, and Destination Benefits. The Case of the 2010 FIFA World Cup in South Africa.” Tourism Management, 34 (2013): 80-90.

83.

Waitt

(2003). “Social Impacts of the Sydney Olympics.” Annals of Tourism Research, 30 (1): 194-215.

84.

Ward

Colleen

Berno

Tracy

. (2011). “Beyond Social Exchange Theory: Attitudes toward Tourists.” Annals of Tourism Research 38 (4): 1556-69.

85.

Weed

(2008). Olympic Tourism. Burlington, MA: Elsevier.

86.

Weed

(2010). “How Will We Know If the London 2012 Olympics and Paralympics Benefit Health?” British Medical Journal, 340: c2202.

87.

Whitson

Horne

(2006). “Underestimated Costs and Overestimated Benefits? Comparing the Outcomes of Sports Mega-Events in Canada and Japan.” Sociological Review, 54 (2): 71-89.

88.

World Health Organization. (1997). WHOQOL: Measuring Quality of Life. Geneva, Switzerland.

89.

Yoon

Gursoy

Chen

S. J.

(2001). “Validating a Tourism Development Theory with Structural Equation Modeling.” Tourism Management, 22 (4): 363-72.

90.

Zhou

Yong

John

. (2009). “Residents’ Perceptions towards the Impacts of the Beijing 2008 Olympic Games.” Journal of Travel Research, 48 (1): 78-91.