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Residents’ perception of the impact of and support for three small and medium-scale sporting events as the basis for a tourism strategy

David Parra Camacho, Juan M. Núñez-Pomar, Ferrán Calabuig Moreno and Paloma Escamilla-Fajardo

Introduction

The majority of studies that analyse the impact of sport events focus on the evaluation of mega-events (Gibson, Kaplanidou and Kang, 2012; Pappas, 2014; Taks, 2013). However, small and medium-sized sport events receive less attention from scholars (Higham and Hinch, 2002), probably due to the traditional view that these events have hardly any economic repercussions (Daniels and Norman, 2003).

Getz and Page (2016) classify different types of sport events, from occasional mega-events such as the Olympic Games, to ‘periodic hallmark events’ such as the world championships of individual sports (e.g., athletics or swimming world trials), regional medium-sized sport events such as national competitions, and small-scale sport events like local competitions (e.g., marathons), being the main characteristic common to all them the temporariness with a set duration, and are thus one-off space-time phenomena.

The minor-scale sport events that Gibson, Kaplanidou and Kang (2012) refer to upon citing Wilson (2006), such as minor events in which the number of participants may exceed the number of spectators, usually take place annually and receive only very minor coverage in the national media, as well as having limited economic impact in comparison with large events. The distinction between small-scale events and large sport events, however, goes beyond the size of the event itself, since the public resource requirements of small events are markedly different from those of hallmark competitions (Gibson, Willming and Holdnak, 2003).

In his definition of small-scale sport events, Higham (1999, p. 87), on the other hand, includes, “regular sport competitions (…), international sport centres, national competitions, masters or sports for disabled persons, and similar”. As a point on attendees of these events Taks (2013) stresses the importance of bearing in mind that, in many cases, the majority of people attending such events come from the local population.

Some authors such as Higham (1999) and Taks (2013) highlight the differences and advantages of small events compared with large sport events. Small sport events are characterised by their efficient use of existing resources, facilities, and infrastructures of the city, which means that public investment is very low compared to that required for large sport events. Furthermore, small events tend to create less disruption for residents in terms of noise, congestion, traffic, and restrictions in access to public facilities and spaces. Moreover, on many occasions, small and medium-sized sport events allow citizens to participate in the event as competitors.

Nonetheless, although they generate less income and attract fewer tourists than mega-events, some scholars point out that smaller cities or regions tend to make better use of the benefits and impacts of small and medium-sized events than in large cities or capitals (Veltri, Miller and Harris, 2009).

Literature review

Residents’ perception of the impact of sporting events

Analysis of residents’ perceptions comes under research into the social impact of sport events, traditionally linked to studies about the social impact of tourism (Fredline, Jago and Deery, 2003). The study of residents’ perceptions on the impact of sport events brings to bear a host of theories that aim to explain changes in residents’ reactions to such events. One of the most widely used is social exchange theory (Kim, Gursoy and Lee, 2006; Ouyang, Gursoy and Sharma, 2017). According to this theory, an exchange process arises in which residents display a favourable attitude towards the event if they perceive that hosting this event will bring more benefits than costs. An alternative theory that is less popular amongst scholars is social representations theory (Fredline and Faulkner, 2000), which holds that citizens have a prior representation of sport events that inform their perceptions regarding the impact of these events, based on the influence of direct prior experience, social interactions, and the media or political groups (Fredline, 2005).

The majority of classifications of the perceived impacts of residents of host communities of sport events derive from the analysis of large sport events (Preuss and Solberg, 2006). Thus, when identifying possible benefits or costs to residents of the hosting of small sport events it is necessary to account for the impacts caused by large sport events. Along these lines, Higham (1999) highlights that smaller-scale events, as opposed to mega-events, have a greater potential to yield positive benefits for the host community, whereas negative impacts are much smaller and easier to control, manage, and minimise.

Residents’ perceptions about economic impacts constitute one of the most cited in the literature (Kim et al., 2015), and are related to employment opportunities, stimulating economic activity, increases in tourism, growth in consumption and citizens’ spending, and improvements in living standards. In contrast, the economic costs of sport events refer to possible tax increases to finance the event (construction and renewal of infrastructure and facilities, security, etc.), shortages of resources, or price hikes in the cost of goods and services (Gursoy and Kendall, 2006). Rises in cost forecasts for financing an event may have negative repercussions in the long term by reducing financing for other activities that require public funds, such as health and education (Solberg and Preuss, 2007).

From the viewpoint of physical impacts, those relating to construction, and improvements to infrastructure and public transport stand out. Nevertheless, the tangible or sustainable urban regeneration impacts or real legacy should be orientated towards the use of facilities or infrastructures after the event takes place (Fredline and Faulkner, 2000).

At the environmental level, numerous authors highlight that events can contribute to the preservation and conservation of patrimony and environment (Lorde, Greenidge and Devonish, 2011; Preuss and Solberg, 2006). Despite this opinion, however, some scholars mention negative environmental impacts associated with pollution, a rise in noise levels, refuse, and potential harm to natural areas (Collins et al., 2007), changes in land use, pollution of the coastline, rivers or lakes, and the deterioration of historical and cultural patrimony (Kim, Gursoy and Lee, 2006).

Another important impact relates to the image and external projection of the host region. Sport events may help promote cities as tourist destinations and boost the city’s image and international standing (Daniels and Norman, 2003). Conversely, poor organisation of an event, inadequate facilities, high prices, or security and crime problems may project a bad image of the city amongst tourists (Solberg and Preuss, 2007).

Socio-cultural impact is usually measured in terms of the increase of local interests and values and of living standards of these events (Guaita-Martínez, Roig-Tierno and Mas-Tur, 2018). Socio-cultural impacts are usually associated with the possibilities that sport events offer the host community to get to know other people and cultures, strengthen local values and traditions, and build national identity, as well as to develop feelings of solidarity and hospitality towards tourists (Zhou and Ap, 2009). Moreover, hosting events presents new opportunities for residents’ entertainment and leisure. Nevertheless, negative impacts arising from hosting sport events include inconveniences to residents, traffic problems, and restricted access to public facilities and spaces. Other potential problems relate to the incidence of crime, vandalism and prostitution (Chen and Tian, 2015).

Positive impacts on a psychological level are associated with a fillip to residents’ pride and feelings of belonging and identification with the community (Kaplanidou, 2012; Mao and Huang, 2016), and the capacity of sport events to create a festive atmosphere amongst the host community throughout the event (Kim and Walker, 2012). On occasions, organisers promote other activities and celebrations to go along with the event and offer entertainment and leisure opportunities to residents (Kim and Walker, 2012). Some negative psychological impacts relate to the possibility that residents adopt a defensive stance towards the host city, area, or region, as well as the scope for misunderstandings between residents and visitors that may lead to conflict or hostility (Preuss and Solberg, 2006). Studies such as Gursoy and Kendall (2006) highlight the importance of the preceding psychological benefits, as they may help citizens to tolerate and accept more willingly the negative socio-economic impacts.

Prior studies

Scholars make contributions through research into the social repercussions of special events that have a lesser international impact, as is the case of the world championships of certain sports such as Tour of Taiwan (Ma and Kaplanidou, 2017), the Superbowl (Kim and Walker, 2012), or the trials of the F1 world championship (Añó, Calabuig and Parra, 2012).

Nonetheless, research in this area that focuses on small and medium-sized events is scarcer. In fact, the majority of contributions focus more on the social impact of small festivals and community events (Fredline, Jago and Deery, 2003; Small, 2008) than sport events (Taks, 2013). Contributions from the point of view of residents’ perceptions of the impacts of small-scale events include Ntloko and Swart (2008), who look at the Red Bull Big Wave in Cape Town (South Africa). More recent work has also analysed the social impact of small-scale events in various regions of the world (Chen, Gursoy and Lau, 2018; Parra et al., 2016; Taks et al., 2016).

Insofar as aspects that residents perceive positively emphasise the economic benefits for local business, entertainment offerings, raising the host region’s profile, and boosting pride and social cohesion within the community (Ntloko and Swart, 2008). Conversely, both Ntloko and Swart (2008) place an emphasis on residents’ perceptions of these events’ economic impacts directly benefiting a small group of stakeholders rather than the local population as a whole. Similarly, the scant participation of the local population in the organisation and planning of the Red Bull Big Wave is another of the negative aspects that residents cite in Ntloko and Swart (2008).

In summary, the literature review reveals a dearth of contributions that analyse residents’ perceptions of the benefits and impacts of small and medium-sized sport events, how these events affect residents’ quality of life, and the factors that determine the attitude of citizens towards such events. The literature review uncovers even fewer articles that compare residents’ perceptions about three small and medium-sized sport events with different characteristics in one locality.

Study approach

This study’s aim is to analyse whether there are differences in residents’ perceptions (benefits and costs) of hosting three sporting events: Valencia Formula 1 Grand Prix (F1GP), the Valencia Open 500 ATP World Tour (O500), and the Valencia Marathon (VM). These three events have markedly different characteristics in terms of type of sport, size and repercussion, proximity to spectators, and the opportunity for members of the local population to participate in the event. The sporting disciplines of the three events are distinct from the spectator’s point of view, and they present different opportunities for residents to participate. Furthermore, the study assesses whether willingness to host the event in subsequent editions influences residents’ perceptions regarding the events, and also detects and verifies which factors or aspects perceived by the residents have the greatest influence, depending on the event.

The F1GP has a significant impact on the international stage, with a greater institutional collaboration than the other two events as regards demands on public resources. Of the three events under study, organisation of the F1GP is the most complex, as it requires setting up stands and greater security, in addition to restricted access to several sections of the Valencian docklands. Furthermore, it attracts the greatest number of spectators, tourists, and residents, but, at the same time, is the least accessible for the local population, on account of the sport’s characteristics and the price of tickets, amongst other factors.

The O500 is a medium-sized event, with less international relevance and a smaller drain on public resources than the F1GP. The number of spectators and competitors are lower than the other two events, although the scope for residents’ participation is practically non-existent. Of the three events under study, the O500 presents the least organisational complexity.

The marathon is a well-established, local event – although it is currently growing its international image – with considerable participation from local residents. Despite the event’s classification as the least elitist and most accessible to the local population of the three, the marathon also requires a considerable coordination effort on the part of local services (local police, civil protection, etc.), owing to the need to delimit an extensive circuit that passes via the main traffic routes of the city.

Method

Participants

A total of 1030 city residents took part in the study, with non-residents being excluded from the sample population. Thus, 980 valid questionnaires provide the raw data for analysis (335 from the F1GP, 352 from the O500, and 293 from the marathon). Respondents have the following gender distribution: F1GP, 39.4% male; O500, 40.3% male; and marathon, 42.7% male. The age of respondents ranges from 16 to 80.

Instrument

The questionnaire comprises a scale of 35 items. These items draw upon previous research in this area, with a special focus on studies on sport events, (Balduck, Maes and Buelens, 2011; Kim and Walker, 2012; Lorde, Greenidge and Devonish, 2011; Ritchie, Shipway and Cleeve, 2009), as well as research into other cultural events and festivals (Small, 2008), and tourism (Ko and Stewart, 2002). The questionnaire items were adapted specifically for our research into the three events currently under study and cover positive and negative impacts deriving from the hosting of these events. The items were then revised by scholars and specialists in research into sport economy and management.

The 35 items form eight initial dimensions: socio-economic impact (4 items); impact on image and promotion (3 items); impact on infrastructure (3 items); socio-cultural impact (5 items); community pride (5 items); social cost (9 items); and impact on sport (6 items). All items use a seven-point Likert-type scale (1 = strongly disagree; 7 = strongly agree). Additionally, the questionnaire includes two dichotomous questions: the first of which refers to the respondent’s support for each event and the second corresponds to whether the respondent regularly engages in some kind of sport activity. Finally, the questionnaire contains several questions that relate to sociodemographic variables.

Procedure and data collection

After we devised the questionnaire, volunteer students from the Department of Physical Education and Sport at the University of Valencia administered the survey to city residents. Prior to data collection, interviewers received training by way of an eight-hour seminar. The interviewers carried out street-level interviews of permanent residents of Valencia across several of the city’s districts. Once interviewers had explained the purpose of the study, the interviewees filled out the questionnaire in situ under the supervision of the interviewers, who were on hand solely to resolve any doubts the interviewee may have.

The interviewers performed the data collection process upon termination of each event for the year 2012. Specifically, data collection took place for the F1GP (last week of June) in September, for the O500 (last week of October), and the marathon (second weekend of November) in December.

Taking a lead from the approach of other studies in this research field (Gursoy and Kendall, 2006; Lorde, Greenidge and Devonish, 2011), we employed a convenience sampling method, given the absence of an adequate sampling frame. To minimise selection bias, interviewers received instructions to collect data from different groups of the population. Furthermore, each interviewer collected data from a different district so as to cover the city’s entire population. In summary, although convenience sampling does not represent the larger population, our sample ensured data capture from a mixture of residents residing in various districts of Valencia.

Data analysis

The questionnaire yielded raw data that was statistically processed using SPSS version 20.0 and EQS 6.2. We first performed confirmatory factor analysis for each sample, taking the dimensions proposed previously as a starting point. Following this, we applied confirmatory factor analysis with multiple samples. We used a range of goodness of fit indices to confirm the model’s fit to the data. Namely, the Root Mean-Square Error of Approximation (RMSEA; Steiger and Lind, 1980), the Non-Normed Fit Index (NNFI; Hu and Bentler, 1995), the Comparative Fit Index (CFI; Bentler, 1990) and the normalised chi-square (NC, χ2/ df; Wheaton et al., 1977). The measures to evaluate the scale’s reliability are Cronbach’s alpha, Composite Reliability (CR), and the Average Variance Extracted (AVE). Furthermore, the significance of factor loadings within the factor’s dimension and the associated t-values determine convergent validity. The discriminant validity was determined using the correlation between the factors and a 95% confidence interval for the correlations between the factor pairs (Anderson and Gerbing, 1988).

After performing the CFA, we calculated the means and standard deviations (SD) for all items that make up each dimension. To analyse the difference of means as a function of event and sociodemographic variables, we carried out differential analysis, using the t-test for independent samples and ANOVA, first applying Levene’s test to verify the homoscedasticity of each variable. Likewise, we applied Tukey’s test to determine the differences between categories or subgroups of variables.

Finally, we calculated three binary logistic regressions to assess the relationship between residents’ support for staging the event, the dimensions derived from the factor analysis, and the sociodemographic variables. We then calculated the odds ratios, the p-values, the Hosmer-Lemeshow test to assess the goodness of fit of the model, and the Nagelkerke R² to identify the percentage of variance that each of the predictor variables explains.

Results

Confirmatory factor analysis with multiple samples

Prior to the confirmatory factor analysis and given that the maximum likelihood is highly sensitive to the absence of normality (Bentler, 2004), we tested for multivariate normality using Mardia’s coefficient. The coefficient exceeded the cut-off value of 5, which led us to use the Satorra-Bentler scaling method (Satorra and Bentler, 1994) and its associated p-value.

Thus, after testing the initial model consisting of seven factors and 35 items, results show that the model offers a poor fit to the three samples. With the aim of improving the model’s overall fit, the number of grouping factors was increased and some items whose indices indicated high residual values and correlations with other items were dropped from the model. This approach led to an increase in the number of factors to eight (splitting the sport factor into two), and a drop in the total number of items to 30 (eliminating one item from the factor of impact on sport, and four items from the factor of social cost). Therefore, the final model comprises eight items and 30 items. For this final model – and following a second round of confirmatory factor analysis applying the robust method for analysis of multiple samples – the chi-squared test yields significant results (χ² = 2456.82, df = 1147, p < .01), and the value of the normalised chi-squared (χ²/df = 2.14) falls below the threshold value of 3 proposed by numerous authors (Bollen, 1989; Kline, 2005). Similarly, the other indices of goodness of fit imply that the model has a good fit with the data [(RMSEA = .059 (Confidence Interval (CI) of = [.056, .062]); NNFI = .92; CFI = .93)]. To ensure the convergent validity of the scale, we checked the values for the three samples of the t-test of each variable, which range between 7.47 and 28.93 (t > 1.96) and are significant at the .05 level. To assess discriminant validity, we verified that all correlations between factors are below .85 (Kline, 2005). We also observe that the value 1 falls outside the confidence interval for the correlations (Anderson and Gerbing, 1988), and that the correlations are below this cut-off value for all intervals. Table 10.1 shows that Cronbach’s α for each factor in each of the three samples is between .68 and .93, whereas the values for the CR are between .67 and .94. Likewise, the values for the AVE range between .43 and .83.

The results in Table 10.2 demonstrate that the dimension of impact on image and promotion of the city contains the highest-scoring items. Conversely, the items that make up the factor of impact on infrastructure have the lowest means for all three sport events. For the social cost dimension, results show that the F1 event has the highest means for all items, whereas, at the other end of the scale, the marathon is the event with the lowest means.

On the other hand, the F1’s scores for the dimension of impact on the image and promotion of the city are the highest of all three events. In contrast, residents’ scores yield higher means for the items that make up the dimension of social cost.

In the case of the O500, the factors of community pride and socio-cultural impact have the highest means. In contrast, residents assign lower scores to aspects that relate to the impact of the infrastructure or event excitement.

Finally, the marathon scores more highly in the dimension of impact on sport promotion and the items belonging to the factor of socio-cultural impact. Conversely, the lowest scores for this event emerge in the factor of residents’ perceptions of the socio-economic impact, and in the dimension of cost.

Table 10.1 Final model with reliability indicators for each dimension, by sport event

Table 10.2Final model with means and standard deviations for each event

Factors	F1	O500	Marathon
Socio-economic impact	M(SD)	M(SD)	M(SD)
Increases business opportunities	4.41(1.77)	4.06(1.61)	4.01(1.64)
Attracts investors in the city	4.07(1.75)	3.74(1.63)	3.57(1.58)
Creates jobs in the city	4.08(1.78)	3.77(1.65)	3.47(1.74)
Creates business opportunities for residents with small businesses	3.85(1.83)	3.66(1.70)	3.95(1.62)
Impact on infrastructure
Means the city develops public infrastructure that citizens subsequently benefit from	3.18(1.81)	3.06(1.75)	3.22(1.70)
Improves public facilities	3.57(1.72)	3.37(1.61)	3.62(1.70)
Improves the quality of public services	3.38(1.72)	3.18(1.63)	3.46(1.70)
Impact on image and promotion
Promotes the city as a tourist destination	5.24(1.59)	4.49(1.60)	4.42(1.66)
Improves the appearance of the city	4.18(1.80)	4.08(1.67)	4.24(1.53)
Media coverage improves the international image of the city	4.94(1.71)	4.34(1.60)	4.49(1.62)
Socio-cultural impact
Allows citizens to meet new people	3.63(1.79)	3.59(1.66)	5.04(1.45)
Gives citizens the chance to attend an international event	4.31(2.00)	4.63(1.74)	4.03(1.62)
Makes it possible to get to know new societies and cultures	3.31(1.72)	3.21(1.56)	4.59(1.58)
Offers citizens an alternative leisure activity	4.23(1.86)	4.17(1.71)	5.40(1.32)
Provides new activities for the city	4.08(1.90)	3.99(1.68)	5.23(1.46)
Community pride
Boosts pride amongst citizens	3.85(1.93)	3.48(1.67)	4.29(1.63)
The city can host other important events	4.07(1.89)	4.19(1.72)	4.59(1.58)
Demonstrates our capacity to host other important events	4.55(1.77)	4.41(1.61)	4.82(1.52)
Improves our image as an important city	4.76(1.80)	4.46(1.65)	4.83(1.62)
Improves national awareness of our city	5.18(1.78)	4.74(1.65)	5.11(1.47)
Social cost
Causes inconvenience to residents	5.04(1.71)	3.54(1.67)	3.76(1.62)
Causes harm to the environment	4.96(1.81)	2.88(1.57)	2.13(1.61)
Causes price increases in goods and services	4.99(1.69)	3.86(1.72)	2.79(1.62)
The construction of facilities for visitors is a waste of tax-payers’ money	5.28(1.79)	4.77(1.92)	3.08(1.78)
The construction of facilities involves excessive spending	5.72(1.65)	5.14(1.87)	3.13(1.78)
Event excitement
I enjoy watching the F1/Tennis/Athletics	4.03(2.08)	4.25(1.92)	4.28(1.83)
Increases my support of F1/Tennis/Athletics	3.33(2.01)	3.43(1.95)	3.90(1.86)
Increases my interest in F1/Tennis/Athletics	3.30(2.02)	3.48(1.96)	3.97(1.85)
Impact on sport promotion
Creates more opportunities for youth sport	2.59(1.77)	3.32(1.79)	5.30(1.36)
The organisers promote sport	3.63(1.92)	4.03(1.70)	5.35(1.38)

Note: M – Mean; SD – Standard Deviation

Comparison of residents’ perceptions by event

Table 10.3 shows the overall mean and standard deviation of the items that compose each dimension. The table also displays the comparisons of residents’ perceptions between each event through ANOVA estimation. The ANOVA technique uncovers statistically significant differences (p<.05) for almost all factors, except for the impact on infrastructure. Tukey’s post hoc test detects significant differences for socio-economic impact and image and promotion of the city between F1 (M = 4.10 and M = 4.79, respectively) and O500 (M = 3.81 and M = 4.30), and between F1 and the marathon (M = 3.77 and M = 4.38), in both cases in favour of the F1. For the factor of socio-cultural impact, differences emerge between the marathon (M = 4.95) and the other two events, in favour of the former. In the case of the community pride factor, the differences are between the marathon (M = 4.73) and the O500 (M = 4.26), in favour of the former. For the event excitement factor, differences appear between the marathon (M = 4.05) and the F1 (M = 3.55), also in favour of the former. For the social cost dimension, results reveal differences between all events, favouring in this case the F1 (M = 5.20) when compared with the other two events, and the O500 (M = 4.04) when compared with the marathon (M = 2.97). Finally, the impact on sport promotion is significantly different for the three events, with the marathon (M = 5.32) scoring favourably with respect to the other two events, and the O500 (M = 3.68) attaining a significantly higher score than the F1 (M = 3.11).

Residents’ perceptions of the impact of staging events, and their willingness to host subsequent editions

We performed three binary logistic regressions to analyse the relationship between the willingness of residents to host the F1, the O500, and the marathon in future editions, the impact factors, and sociodemographic variables. In the regression model for each event, the predictor variables were the eight factors derived from the multi-group confirmatory factor analysis and the sociodemographic variables, whereas the willingness to host the event in subsequent editions is the criterion variable.

Table 10.3 ANOVA of one factor to compare residents’ perceptions of impact for each event

Table 10.4 Results of the binary logistic regression between event backing and residents’ perceptions of the impact of sport evens

In the three regressions, the statistical analysis implies a good fit of the model, according to the Hosmer-Lemeshow test for both the F1 (χ² = 5.96, p = .65) and the O500 (χ² = 4.87, p = .77). In the case of the marathon, the first regression model with all the predictor variables offers a poor fit according to the Hosmer-Lemeshow test (χ² = 20.74, p = .01). Therefore, because of this result, we eliminated the gender variable to improve marathon’s fit (χ² = 4.51, p = .81). The Nagelkerke R² statistic indicates that the variables included in the model explain 66.1% of the variance in the case of the F1, 73.5% for the O500, and 63.6% for the marathon.

Results show numerous factors that reveal significant differences in residents’ backing of hosting future sport events. In the case of the F1, for each unit increment in residents’ perceptions of socio-economic impact, impact on image and promotion, and event excitement, the probability that residents display willingness to back the organisation of an event in subsequent editions (compared with not backing the event) increases by 1.93, 1.89, and 1.54, respectively. For the O500, a unit increment in residents’ perceptions of the socio-economic impact, community pride, and event excitement imply that the probability that residents will support the hosting of a future edition of the event increase by 1.59, 2.30, and 1.82, respectively. The significant relationships in the case of the marathon emerge for the factors of community pride, event excitement, and impact on sport promotion, with an increase in willingness to host this event in future editions of 2.26, 2.04, and 1.95, respectively. Conversely, for each unit increment in the factor of social costs, the probability of residents supporting the hosting of the event diminish by .60 in the case of the F1, .40 in the case of the O500, and .57 for the marathon.

Finally, the sociodemographic variables share no significant relationships (p<.05) with backing the hosting of sport events, except in the case of the F1, for which for each unit increment of the gender variable (male), the probability of supporting the even in future editions diminishes by .36.

Discussion and conclusions

The perception of the host community seems to play a fundamental role when it comes to promoting sports event tourism. Literature already points out the importance of approaching from different perspectives the study of tourism marketing in general, and experiential tourism, as is the case of sports tourism, in particular (Tsiotsou and Ratten, 2010).

In this sense, our study can constitute a singular contribution to the understanding of a relevant aspect for the design of a tourism strategy based on tourism of sporting events. To do so, we developed and adapted from previous studies an instrument designed to assess the opinion of residents about these events. Following the data collection process, the instrument was subjected to several rounds of confirmatory factor analysis for each sample, and, subsequently, another round of confirmatory factor analysis for multiple samples. The results of this analysis yield a scale consisting of 30 items and eight factors, whose labelling was carefully chosen on the basis of the characteristics of the items and with reference to previous studies in this research area.

The labelling of the dimensions is similar to that employed in other studies in this area: socio-economic impact (Kim et al., 2015); impact on image and promotion of the city (Kim and Petrick, 2005); impact on infrastructure (Kaplanidou et al., 2013); socio-cultural impact (Gursoy et al., 2011); community pride (Kim and Walker, 2012); social cost (Kaplanidou et al., 2013); and event excitement (Kim and Walker, 2012). Our study diverges slightly from the traditional approach in other studies that usually include the dimension of impact on sport promotion in factors such as those that relate to positive social impacts (Ritchie, Shipway and Cleeve, 2009).

In general, residents perceive that the events under study have no impact on the city’s infrastructure, as scores for this factor are generally lower than other factors and with a tendency to disagree. As this study looks at small- and medium-scale events, as stated previously, the construction of new infrastructure and public services is generally low, owing to this type of event’s use of existing infrastructure, hence little requirement for investment in this area (Taks, 2013). In fact, the greatest investment in this area comes from the F1GP, although these investments have no direct benefit for citizens, given that they focus on constructing the race circuit and adapting it to the city. Despite this focus on spending on the event itself, as numerous authors point out (Fredline, 2005), residents usually demand that new facilities and infrastructures that are built as a consequence, either partially or totally, of a sport event later serve the community in some way.

The aspects related to the image and promotion of the city as a consequence of staging these events are the ones that yield the highest scores, supporting findings from previous research (Balduck, Maes and Buelens, 2011). In terms of this dimension, the most highly rated event according to residents is the F1, especially in aspects relating to the promotion of the city as a tourist destination and the increase in media coverage. Of the three events under study, the F1 has the greatest international impact, and the media coverage this event receives allows the city to project and advertise itself as a tourist destination.

Residents tend to perceive the aspects within the factor of socio-cultural impact and community pride more positively in the case of the marathon. This may share an association with a widespread perception that this event is closer to and more popular amongst citizens. Indeed, the marathon is an event that gives residents greater opportunities for active participation as runners and organisers of the event alike. This aspect of the event gives rise to awareness and interaction amongst residents, and other participants or visitors. Several studies find that smaller events offer new opportunities for training, boost social cohesion, hospitality and solidarity with visitors, and improve community pride amongst residents (Ntloko and Swart, 2008; Taks, 2013). In turn, this may explain respondents’ tendency to score this event highly in the dimension of impact on sport promotion.

Residents consider the F1GP to be the event that runs up the biggest costs, followed by the O500. Their opinion of the marathon is quite different, with results showing that residents disagree that the event incurs many of the costs cited in the questionnaire. For the F1, the residents are most critical of costs relating to the construction of facilities and provisions for visitors, as well as those that relate to inconveniences for citizens due to hosting the event. In the case of the O500, residents stress the costs associated with the construction of facilities and provisions for visitors. For this reason, above all in the case of the F1 – the sport event that requires the biggest budget – organisers should propose actions that minimise negative impacts or compensate, as far as possible, the problems that residents suffer by hosting this event, paying special attention to the most affected groups such as residents of the neighbourhoods closest to the circuit.

Furthermore, the results of the study support the assertion that the residents’ perceptions of the impacts influence their willingness to host sport events in future editions. In the case of the F1GP and the O500, a positive tendency in residents’ perception of the socio-economic impact and the level of event excitement influence the residents’ backing of future editions of the events. For the marathon, results show that the aspects that influence support for future events relate to community pride, the level of event enthusiasm, and the impact on sport promotion. Conversely, an increment in residents’ perceptions of the social costs linked to hosting an event reduces their willingness to host the event in the future. These results corroborate the findings of other research in this area, albeit with a majority that refer to larger events (Balduck, Maes and Buelens, 2011; Fredline, 2004; Gursoy and Kendall, 2006; Kaplanidou et al., 2013).

In summary, for citizens of Valencia, the F1GP and the O500 have great potential as enhancers of the external image of the city, but are perceived by residents as somewhat elite events, with scarce direct, short-term benefits for residents. As Fredline (2005) expounds, it is important for organisers and authorities to develop an adequate communication strategy with the aim of shifting benefits and positive impacts of hosting these events to residents of the host city. For that, studying the identity of consumers with sport is a vital element to commercialise it in more effective ways (Ratten, 2011). At the same time, they must ensure the minimisation of impacts that residents perceive as negative. Along these lines, Chalip, Green and Hill (2003) highlight that the political and economic viability of a sport event in the long term depends greatly on a good marketing and communication strategy, which should stem from the host community itself. This action, together with an increment in the participation of residents in this type of event, minimising the negative impacts, can contribute to improving residents’ perceptions of such events. Chien et al. (2011) find that the degree of support for hosting an event is greater when residents view the event positively, with the media providing a useful tool in the promotion of the community.

Limitations and future research

First, the scale used in this research could be expanded to incorporate new factors relating to psychological, environmental, and political impacts, with the aim of observing the relationship these factors have with a willingness to support small- and medium-scale events. It would also be of interest to extend the number of items in the dimension of impact on sport promotion to endow this factor with more content. Furthermore, scholars should exercise caution when interpreting the results of this study, avoiding generalisations to the population as a whole, given that the study methodology employs a convenience sampling frame.

Future research could analyse the changes in perceptions over time, so as to gain an understanding of the long-term benefits of these events. Scholars should also seek to carry out studies that identify subgroups of the population with similar perceptions of this type of events because different members of the community may have different views regarding the events’ negative and positive impacts. Furthermore, researchers should pay attention to differences depending on sociodemographic characteristics and other intrinsic variables such as interest in this type of event, attendance, participation, or having some kind of economic stake in these events.

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