Given the massive investments that are made in the development and in the execution of mega-events, coupled with the planned development goals of such events, there is a need for a holistic evaluation of such events in order to assess the range of benefits and costs that are delivered by the event (Jago et al., 2010). In the literature that is available on mega-event
valuations, most have tended to focus on the short-term economic impact of the events (Maennig and Zimbalist, 2012a; 2012b; 2012c), while neglecting the more intangible social impacts, such as the feel-good effects (Maennig and du Plessis, 2011). Within this context, Jago et al. (2010: 224) argue that “unfortunately, the economic evaluation of mega-events has often fallen short of ‘state of the art’ assessment standards”. In most of her analysis of mega- events, including the reasons for wanting to host them, Cornelissen (2004a; 2004b; 2006;
2007a; 2007b; 2008a; 2009; 2010a; 2010b) details how the economic evaluations undertaken to date have largely been criticised for having being overstated by politicians wanting to justify the hosting of the event.
Event impacts can be evaluated by means of a variety of methods. One way of evaluating the impacts of mega-events is by using the Balanced Scorecard Approach, which was developed and proposed by Gratton et al. (2006). The approach was developed to consider the strong evidence that had been suggested by empirically based research which shed light on the fact that, besides economic impacts, events tend to bring about a range of other socio-cultural and environment-related impacts.
Figure 3.8: The ‘balanced scorecard’ approach to evaluating events Source: Gratton et al. (2006)
Event aims
Place marketing effects
Sports development Media and sponsor
evaluation Economic impact
Figure 3.8 above, which was developed by Gratton et al. (2006), considers that the media coverage of an event at the local and international level also adds value to the event in terms of the benefits accrued. The place marketing benefits of the event are noted as influencing the key sectors of the destination. For example, the increased media coverage would be likely to influence the tourism by increasing the number of visitors at the destination. Additionally, the public perceptions of the event host destination might also improve which, in turn, might lead to repeat tourism arrivals being buoyed up by the word-of-mouth feedback from the spectators or attendees of the event. The figure further suggests that another immediate benefit to come from the staging of a sport event might encompass some sport development impact. Such an impact might involve improved participation in sport by the local community members in the host community. “The long-term effect of any increase in participation could be tracked, although it may be difficult to prove causality” (Gratton et al., 2006: 54).
Another method that has been suggested to measure mega-event legacies is that of the top- down and bottom-up approaches (Preuss, 2007). The top-down approach compares the economic variables of the city/ region that staged an event with the same variables of a city/
region not staging the event. In this approach, the event legacy is considered as the difference between the ‘event case’ and the ‘without case’ (Gratton and Preuss, 2008; Preuss, 2007). In the bottom-up approach, which is based on the long-term development plan for a city, all changes of structure due to the event are considered. Consequently, the plans for future city development represent the ‘without case’, being the city development that will take place without the event (Gratton and Preuss, 2008; Preuss, 2007).
Dickson et al. (2011) developed a framework for evaluating Olympic and Paralympic legacies. To demonstrate how the legacy radar framework might be applied, three scenarios of potential legacies from mega sport-tourism events are considered (see Figure 3.9 below).
Dickson et al. (2011: 295) argue that the flexibility of the legacy radar makes it is possible to
“rate legacies from negative to positive with one end of the Likert scale being descriptors or measures of negative legacies and the other end positive descriptors”. Moreover, the planning dimension could range from the unplanned to the highly planned, as with venues and transport. It is also possible to expand the number of items in the scale.
Figure 3.9: A comparison of three legacy profiles: the legacy radar diagram Source: Dickson et al. (2011)
According to Preuss et al. (2007: 6), “a well-known method of measuring the benefits of events is known as cost-benefits-analysis where the direct, indirect and tangible costs and benefits of an event are measured”. These cost–benefit assessments should include several sources of information about the visitors, in terms of a number of specific variables. However, it is important to note that ticket revenue and expenditures differs among the visitors. Thus, Jago et al. (2010: 232) caution that it is important that costs and benefits are considered, rather than simply impacts. The social return from events and their contributions towards sustainability are important factors. However, the cost–benefit analysis discourse, according to Preuss et al. (2007), is complex in terms of measurement, due to the fact that the contributions that are made by the visitors to a mega-event are often miscalculated.
Each of the methodologies presented above has its own strengths and limitations, but can be useful and applied based on event uniqueness and complexities, as well as on the times and the different cities/ countries involved (Preuss, 2007). However, several researchers have motivated for the evaluation of the long-term benefits and costs that are associated with the hosting of mega-events as a useful means of planning for the execution of future mega-events.
Using such a methodology might serve to provide support for, and to inform, decision-making for countries that seek to attract such events in future in terms of how to maximise the opportunities presented by the event (Cornelissen et al., 2011; Gratton and Preuss, 2008;
Leopkey and Parent, 2012; Maennig and Zimbalist, 2012c; Preuss, 2011; 2013; Preuss et al,.