Understanding Residents’ Perception Changes toward a Mega-Event through a Dual-Theory Lens

Abstract

This study attempts to integrate social exchange theory (SET) and social representations theory (SRT) in understanding residents’ perception changes throughout a mega-event’s full life cycle. A three-wave survey approach was employed to track local residents’ evolving attitudes toward the 2010 Shanghai Expo. Results showed that Shanghai residents’ perceived impacts at the community level were more positive than at the individual level. Residents’ visit status (i.e., whether they attended the event or not) did not appear to substantially influence their perceptions of impacts at either level, albeit attendees did demonstrate more favorable attitudes on several aspects. Most residents held similar perceptions at the beginning of and six months after the Expo had concluded, but their perceptions at the Expo’s conclusion were the most negative. This study supports that SET and SRT are distinctly operative in different contexts but can complement each other in explaining residents’ perception changes.

Keywords

resident perceptions mega-events social exchange theory social representations theory Shanghai Expo

Introduction

Event-induced tourism has been growing fast in the recent decades because of various reasons. From the demand perspective, events provide unique experiences that are never truly repeatable, which appeal to a huge market base (Getz 2008). From the supply side, hosting events is considered an effective approach to promoting tourism, as the man-made appeals generated by events tend to enhance the overall attractiveness of destinations (Ritchie 1984). Moreover, events are one of the few tourism products that destination marketing organizations (DMOs) can directly engage in and control (Getz, Anderson, and Sheehan 1998) and are hence highly valued for their role “as attractions, catalysts, animators, place marketers, and image-makers” (Getz 2008, p. 406).

Nevertheless, hosting events could add much pressure to a locale’s infrastructure and resources for a short period of time, which could have a long-lasting effect on the host community. Because local residents are among the key stakeholders of tourism development and their attitudes play an important role in the fate of a tourism project, understanding residents’ perception of tourism developments is critical to policy makers, tourism developers and planners, and tourism researchers (Harrill 2004). Further, hosting events today, particularly major ones, requires huge resource commitment. Governments, destination marketers, and event organizers need a better understanding of the impacts brought by an event, because they are accountable for the consequences of staging it. Thus, empirically investigating local residents’ perceived impact of events will contribute to not only the tourism literature but also destination marketers’ decision-making and resource allocation practices.

Traditionally, most academic research on event impacts has studied sports-related mega-events such as the Olympics and the World Cup, while nonsports events like the World Expos—one of the largest contemporary events worldwide (Bureau International des Expositions, BIE, N/D)—have received less attention (Getz 2008). In addition to its scale, the World Expos is also unique for its six-month length, which not only allows researchers to examine effects usually not detectable in shorter events, but also affords them the feasibility of studying event-related phenomena throughout its full life cycle. The World Expo 2010 Shanghai China (hereafter referred to as “Shanghai Expo” or “the Expo”), reportedly the largest Expo ever (Branigan 2010), provides an opportunity to fill this research gap.

From a methodological perspective, most previous studies on resident attitudes or event impacts employed one-off, cross-sectional surveys, whereas longitudinal studies including a postevent assessment are still rare. Because the local community’s attitudes could fluctuate at different stages (Faulkner et al. 2000), reflecting an adaptation (Doxey 1975) or acculturation process (Hofstede 1997), monitoring resident perception changes over time could provide new insight in this regard.

Given the importance of understanding resident perceptions, the lack of such studies on World Expos, and the methodological shortcomings of previous event impact research, this study aims to track perception evolvement, if any, among residents of Shanghai where the 2010 World Expo was held. Three rounds of survey were conducted at the beginning, the end, and six months after the event, to follow resident perception changes over the life cycle of Shanghai Expo. Two potentially complementary theories, that is, social exchange theory (SET) and social representations theory (SRT), were used in analyzing and interpreting residents’ perception changes. In exploring the distinction and connection of the two theories, the authors concur with Pham (2013), who suggests that “many theories should be seen as complementary rather than competing in that they represent different levels of explanation. . . . We should be open to the co-existence of multiple theories rather than feel the constant urge to identify a single ‘best’ theory.” Thus, this study may also serve as a plea for better appreciation of the complementary nature of selected theories in understanding tourism phenomena.

Literature Review

Tourism impacts on the host community form one of the most important research areas in the tourism literature (Xiao and Smith 2006). Among numerous studies on the social, cultural, economic, and environmental impacts associated with various types of tourism development, one line of research focusing on tourism events’ impacts seems to have attracted particular attention (Fredline and Faulkner 2000; Gelan 2003; Gursoy and Kendall 2006; Long and Perdue 1990; Ritchie and Aitken 1984; Ritchie and Lyons 1990; Waitt 2003; Weaver and Lawton 2013; Zhou and Ap 2009). Other than their relatively short duration and purely man-made nature, tourism events appear to share commonality with other tourism development projects. Thus, it appears mainstream theoretical frameworks of resident attitude studies should be applicable to studying resident attitudes toward events.

Social Exchange Theory

A number of theoretical approaches have been employed by previous studies on resident attitudes toward tourism (Harrill 2004; Nunkoo, Smith, and Ramkissoon 2012; Vargas-Sánchez, Porras-Bueno and Plaza-Mejía 2011). Many earlier studies on the topic were theoretically indebted to SET (Ap 1992; Jurowski, Uysal, and Williams 1997), which views social relation building as a process of resource exchanges, and residents’ reactions as the outcome of a calculated evaluation between one’s (expected) costs and (expected) benefits (Ap 1992).

According to SET (Homans 1958; Skidmore 1975), individuals will engage in exchanges if “(1) the resulting rewards are valued; (2) the exchange is likely to produce valued rewards; (3) perceived costs do not exceed perceived rewards” (Jurowski, Uysal, and Williams 1997, p. 3). Essentially, SET takes an economics approach and assumes that human interactions involve rational cost–benefit evaluations out of self-interest and to maximize rewards (West and Turner 2004). As a result, despite being a sociology theory in origin, SET and the notion of “exchanges” have been particularly influential in business-related studies (Bagozzi 1975; Cropanzano and Mitchell 2005).

SET is not merely one theory but represents a conceptual paradigm (Cropanzano and Mitchell 2005) bridging key premises from alliance theory (Levi-Strauss 1969), interdependence theory (Kelley and Thibaut 1978; Thibaut and Kelley 1959), resource theory (Foa and Foa 1980), and so on. As an overarching framework, SET involves a set of ideas surrounding the rules and norms of exchange (particularly the principles of reciprocity and negotiation), the resources exchanged (e.g., money and services), and social exchange relationships (Cropanzano and Mitchell 2005). As such, SET has engendered many micro theories focusing on various processes and dimensions of human behavior (Cook and Rice 2006). These micro theories generally converge on one core tenet: “Social exchange comprises actions contingent on the rewarding reactions of others, which over time provide for mutually and rewarding transactions and relationships” (Cropanzano and Mitchell 2005, p. 890). It appears that most tourism studies employing SET are framed on the basis of this principle.

In tourism/event contexts, SET suggests that local residents form their attitudes toward tourism-/event-related impacts based on experiential or psychological outcomes associated with the event (Waitt 2003). Past research found that individuals tend to be more supportive if they perceive the benefits from hosting an event/tourism development as greater than the costs (Gursoy and Rutherford 2004; Jurowski, Uysal, and Williams 1997; Perdue, Long, and Allen 1990). Likewise, if residents view the event or tourism project as bearing unacceptable costs, or the costs outweigh the benefits, they are apt to hold a negative attitude.

Despite its popularity and wide acceptance, SET has drawn some criticisms (Ward and Berno 2011; Woosnam 2011). For instance, it has been criticized for reducing human interaction to rational, economic calculations (Miller 2005; West and Turner 2004). Moreover, SET focuses on fulfilling individual needs from an individualist perspective, which may not be applicable to group behavior, and could face paradigm incommensurability when applied in collectivist cultures (Miller 2005; Pearce, Moscardo, and Ross 1996; West and Turner 2004). Further, one may argue that many residents’ knowledge about a tourism project/event is not based on direct experiences, but socially derived and context driven (Fredline and Faulkner 2000). Hence, some researchers propose SRT as an alternative theory for understanding tourism/event impacts (Fredline and Faulkner 2000; Pearce, Moscardo, and Ross 1996).

Social Representations Theory

SRT, originated from Moscovici (1981), was introduced to the tourism literature by Pearce, Moscardo, and Ross (1996). They suggested that SRT provided a more suitable framework for understanding how values and attitudes towards tourism phenomena are shared within a community. Social representations are “systems of preconceptions, images and values which have their own cultural meaning and persist independently of individual experience” (Moscovici 1982, p. 122).

Social representations serve two purposes. First, they are enabling mechanisms that help people make sense of the material and social world around them. When groups encounter new social issues or phenomena, these mechanisms function as “reference points” seeking to make a similar comparison against one’s own prior knowledge based on past experiences, social interaction, or other sources (e.g., the media). They thereby act as a filter—one that is often resistant to change (De Paolis 1990; Fredline 2004). The latter two sources are particularly important because they are alternative sources from which representations may be “borrowed,” allowing one to modify facts that are inconsistent with one’s perceptions (Moscovici 1982).

Second, social representations are enablers of communication shared by the same members of a society or community, facilitating tacit understandings and “providing them with a code for social exchange and a code for naming and classifying unambiguously the various aspects of their world and their individual and group history” (Moscovici 1973, p. xiii). The key to discerning representations in the community is to identify patterns of consensus or commonality in resident perceptions (Pearce, Moscardo and Ross 1996).

In the tourism literature, SRT has been used to explain community attitudes toward tourism development (Andriotis and Vaughan 2003; Moscardo 2011). For instance, Fredline and Faulkner (2000) drew on SRT to focus on resident reactions to a major motor sport event. Results yielded five different ways of perceiving this event among residents. A follow-up comparative study examined host community reactions toward two Australian motor sporting events (Fredline and Faulkner 2002). Results supported the SRT to the extent that five internally homogenous subgroups of the community were identified, and the reactions of subgroups were found to be quite different at each event with respect to direct experience. Values (e.g., community attachment, participation) also influenced overall perceptions of these events.

Zhou and Ap’s (2009) pre–Beijing Olympic Games study found that both the “embracer” and “tolerator” clusters were unified and undiversified with respect to their favorable perceptions/attitudes about government performance and their preference for tourism development. The authors suggested that government propaganda and the education system played a role in shaping resident perceptions in China. Another study on public attitudes on Olympic legacies (CRD 2007) found that respondents drew heavily on their knowledge of previous Olympics’ legacies to inform their opinion about the 2012 London Olympic Games.

Comparing to SET, SRT appears to be more controversial and less validated. Voelklein and Howarth (2005) summarized SRT’s four major criticisms as vague definition, social determinism, cognitive reductionism, and lacking a critical agenda. Moscovici (1988), pioneer and champion of SRT, argues that the theory aims to provide a macro, conceptual framework rather than identify a set of testable hypotheses. As such, most researchers to date used SRT mainly in interpreting findings rather than building models (Woosnam and Norman 2010).

Conceptual Development

The preceding review shows that SET and SRT have both been applied to and validated in understanding residents’ perceived impacts and attitudes toward tourism/mega-events. The two theories are distinctive in respectively allowing for rational information processing based on assessments of cost and benefit, and subjective reactions to tourism based on one’s own social values. Nevertheless, the two may infiltrate and influence each other (Meng and Li 2011b), as with an individual who considers the views of friends and media as well as (to different extents) his or her own calculations of personal loss and gain. The authors suggest that respectively SET and SRT could be more useful in certain contexts than others; when used together, they may help draw the bigger picture better. For instance, when asked to evaluate an event’s impacts on one’s own life, an individual may go through a calculated cost–benefit analysis based on his or her experiences, particularly if this individual is highly involved in the event (e.g., she/he volunteers for or attends the event). This may also be true when an event is so large (e.g., a World Expo) that most locals inevitably get exposure and experience personal gains or loss associated with the event. The SET mechanism may be in play in such scenarios.

Alternatively, individuals’ responses are more likely to be based on their preconceptions (1) when asked about an event’s overall impacts on the whole community, (2) when the event being assessed has not happened yet or happened long ago, or (3) when the respondent is not that involved in the event (e.g., she/he has not viewed or attended the event). In these cases, locals’ perceived impacts of a mega-event tend to be a function of their social representations of the event, which may or may not reflect the reality.

Combined, the authors view SET and SRT as complementing, rather than competing, theories. Few studies, however, explicitly incorporate both theories into their methodological frameworks. Meng and Li’s (2011b) postevent examination on local residents’ attitudes toward the 2008 Beijing Olympic Games showed that SET is more helpful in explaining perceived impacts on one’s life, whereas SRT is useful in understanding impacts on the host population as a whole. This study goes one step further and attempts to examine the potential theoretical reasons behind local residents’ evolving attitudes toward an event over time, and compare event participants’ and nonparticipants’ (both local residents) perceptual differences.

Research Context and Hypotheses

The Shanghai Expo was held from May 1 to October 31, 2010. It was the first time that a World Expo was held in a developing country. The Shanghai Expo was the largest site (i.e.,528 ha) and most expensive World Expo ever—the total costs could be as high as US$45 billion when taking into account city remodeling (Branigan 2010). Moreover, the Shanghai Expo drew the largest number of participants (with more than 191 countries and over 50 international organizations) and was the most visited World Expo (73 million people attended over the course of 184 exhibition days) in history (Galea 2010). The event and associated cultural program included a myriad of shows, activities, and publications (Busa 2011).

Doubtless, an event of such magnitude created tremendous challenges to an already densely populated city of nearly 20 million people. Further, unlike most other events, a World Expo lasts six months, which causes constant pressure on the city’s infrastructure and service systems. Research on the Olympic Games has shown that the short duration of the event could limit its overall impact (Faulkner et al. 2000). This is certainly not the case with World Expos. For local residents, the initial excitement and pride of staging a World Expo could later be replaced by frustration, even hostility. Tracking such changes may help practitioners better understand and optimize the positive and mitigate the negative impacts.

Finally, although researchers have called for more systematic and longitudinal studies on the impacts of mega-events (Faulkner et al. 2000; Kang and Perdue 1994), few studies have actually employed a longitudinal approach (see Ritchie and Smith 1991, for a laudable exception). To this end, the authors conducted a three-wave study on the residents’ perceived socioeconomic impacts of the Shanghai Expo.

Following Meng and Li (2011b), the authors posited that SET and SRT may complement each other when applied to different contexts (see the Conceptual Development section). Specifically, it is hypothesized that residents’ perceptions of an event are more likely to be built through a SET-based mechanism when the impact discussed is on one’s own life, when respondents directly participate in the event, and when they are most familiar with it (e.g., during the event). In contrast, SRT is applicable when respondents perceive the impacts at a community level, when they have not participated in the event, and when they know little about the event (e.g., before or right after the event begins, or long after the event has ended). Thus, a series of hypotheses were developed corresponding to the earlier discussions:

Hypothesis 1a: Residents’ perceived social impacts of the Expo at individual and community levels were significantly different.

Hypothesis 1b: Residents’ perceived economic impacts of the Expo at individual and community levels were significantly different.

Hypothesis 2a: Residents who attended and did not attend the Expo differ significantly in their perceived social impacts of the Expo at the individual level.

Hypothesis 2b: Residents who attended and did not attend the Expo differ significantly in their perceived economic impacts of the Expo at the individual level.

Hypothesis 2c: Residents who attended and did not attend the Expo differ significantly in their perceived social impacts of the Expo at the community level.

Hypothesis 2d: Residents who attended and did not attend the Expo differ significantly in their perceived economic impacts of the Expo at the community level.

Hypothesis 3a: Residents’ perceived social impacts of the Expo at the individual level were significantly different at the beginning, end of, and six months after the Expo.

Hypothesis 3b: Residents’ perceived economic impacts of the Expo at the individual level were significantly different at the beginning, end of, and six months after the Expo.

Hypothesis 3c: Residents’ perceived social impacts of the Expo at the community level were significantly different at the beginning, end of, and six months after the Expo.

Hypothesis 3d: Residents’ perceived economic impacts of the Expo at the community level were significantly different at the beginning, end of, and six months after the Expo.

Research Methods

The researchers hired a professional marketing research company and conducted phone surveys in Shanghai at the beginning (10 days after the Expo started), end (10 days before the Expo completed), and six months after the Expo. The three waves of data collection were spaced six months apart to capture the initial, right after, and sustained impact perceptions.

The survey instrument on social and economic impacts largely replicated that of Meng and Li (2011b), which was derived from existing scales measuring economic impacts (Ritchie and Aitken 1984; Ritchie and Lyons 1990; Fredline and Faulkner 2002; Fredline, Jago, and Deery 2003) and social impacts (Fredline and Faulkner 2002; Fredline, Jago and Deery 2003; Weaver and Lawton 2001; Wang and Pfister 2008). For each of the economic (seven items) and social (20 items) impact items, the respondents were first asked their level of agreement (5 = strongly agree, 1 = strongly disagree, and 99 = not sure) with general statements regarding the impacts of the Expo (i.e., perceived impacts at community level). Subsequently, they were asked to indicate how the economic/social consequences affected their own life either positively or negatively (i.e., perceived impacts at the individual level), with 5 = very positive, 1 = very negative, and 99 = not sure.

The target population comprised adult (aged ≥18) residents who had lived in Shanghai for at least one year and who stayed in Shanghai for most of the Expo (at least three months from May 1 to October 31, 2010). Following previous studies (Meng and Li 2011b; Zhou and Ap 2009), a proportional stratified random sample of 350 respondents was obtained in each wave via random digit dialing to phone numbers in Shanghai’s 12 urban districts. A detailed sampling plan was developed before Wave 1 based on the latest population statistics available (Shanghai Statistical Bureau 2008); throughout the survey process, the sample demographics was closely monitored and balanced on age, gender, household income, and geographic (district) distribution (Shanghai Statistical Bureau 2008).

All phone surveys were conducted in the company’s CATI center. Standard training and guidelines were given to all callers involved in this project. Once a phone call went through, the caller would briefly explain the project purpose, and invite the household member who was 18 or older and whose birthday was closest to January to participate. If this individual agreed to take the survey, the caller would start from several screening questions to ensure he/she meets the aforementioned target population criteria. Qualified respondents needed to answer a series of questions regarding their perceived social and economic impacts of the Expo, overall attitude toward the event, travel experiences, and demographic characteristics. Most questions were close-ended, such as multiple-choice, Likert, or semantic differential scale. There were also some open-ended questions that allowed respondents to respond freely. On average, the survey lasted approximately 20 to 25 minutes. Comparing to other data collection techniques, phone survey enjoys advantages in speed, geographical flexibility, cost, and fieldwork supervision, but suffers limitations in versatility of questioning and length of the questionnaire (Zikmund 2008).

The effective response rate (among eligible individuals) of the Wave 1 survey was 46.4%. Of the 5,521 numbers dialed, 3,406 were answered by a household member—in other cases, either no person answered the phone after three tries or they turned out to be business or fax numbers. Of those, 1,091 potential respondents of 18 years and older were reached, with 337 not meeting the sampling conditions; hence the effective response rate was calculated as 350/(1,091 – 337). Similarly, the effective response rates were 48.8% for Wave 2 and 50.9% for Wave 3. The fieldwork of each wave lasted about two weeks.

Descriptive statistics were calculated to provide a profile of respondents. For focal items assessed on a 5-point scale, no missing data (“99”) were recorded probably because the questions were easy to understand and respondents were eager to share their opinions. Before testing the hypotheses, the authors first used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for data reduction of social impact items: The EFA was conducted using the Wave 1 social impact items at the community level; the derived factor structure was verified with CFA on the remaining five sets of data (i.e., Wave 1 individual, Wave 2 community, Wave 2 individual, Wave 3 community, and Wave 3 individual). To test Hypothesis 1, paired t-tests were conducted. Independent samples t-tests and analyses of variance (ANOVAs) were used to examine hypotheses 2 and 3, respectively.

Results

As designed, the demographic profiles of the three samples were reasonably consistent (Table 1). It appears most respondents were married (75.7%–81.4% in the three waves), worked full- or part-time (58.9%–64.3%), and were aged between 30 and 59 (59.4%–60.9%). Roughly half of the respondents had some college education or higher (42.3%–51.7%) and had a monthly household income of renminbi 4,000–9,999 (US$586–1,464) (55.4%–55.7%). Altogether, 60% of the respondents visited the Expo.

Table 1.

Respondent Profiles (%).

Characteristic	Wave 1 (n = 350)	Wave 2 (n = 350)	Wave 3 (n = 350)
Gender
Male	50	50	50
Female	50	50	50
Age
18–19	1.7	0.6	2.0
20–24	7.4	8.6	6.9
25–29	7.7	7.4	7.7
30–34	10.3	10.6	10.3
35–39	12.0	12.3	12.0
40–49	21.4	22.3	21.7
50–59	15.7	15.7	15.4
60–64	7.7	8.0	8.3
≥65	16.0	14.6	15.7
Marital status
Single/never married	19.7	20.3	14.6
Married	77.1	75.7	81.4
Separated/divorced/widowed	3.1	2.6	4.0
Other	0.0	1.4	0.0
Education
High school or less	44.6	35.1	42.3
Technical/vocational high school	12.6	13.1	15.4
Associated degree/some college	29.7	34.0	27.7
College graduate	12.0	14.3	14.0
Graduate work/master/doctoral	1.1	3.4	0.6
Employment
Full/part time	64.3	58.9	59.7
Homemaker	3.4	3.7	4.0
Temporary unemployed	1.4	5.7	3.4
Retired	26.6	29.4	28.9
Student	4.3	2.3	3.7
Other	0.0	0.0	0.3
Monthly household income (RMB)
<2,000 (<US$293)^a	2.9	3.0	2.6
2,000–3,999 (US$293–586)	29.4	29.6	30.0
4,000–6,999 (US$586–1,025)	40.3	40.0	39.7
7,000–9,999 (US$1,025–1,464)	15.4	15.6	15.7
10,000–19,999 (US$1,464–2,929)	10.3	8.1	8.6
≥20,000 (≥US$2,929)	1.7	5.7	3.5
Visitation status
Visited Expo	17.4	76.6	86.0
Not visited Expo	82.6	23.4	14.0

Based on the RMB–U.S. dollar exchange rate in May 2010.

The EFA of social impact items (Table 2) resulted in two dimensions, “positive social impacts” and “negative social impacts,” using Wave 1 data at the community level. The item “improved the volunteerism—the local social support networks” was deleted because of cross loading. The item “caused displacement/relocation or removal of local residents” was removed because of its factor loading of lower than 0.5 and to enhance reliability of the negative social impacts factor.

Table 2.

Factor Analysis of Social Impacts (Wave 1, community level).

Item/Factor	Factor Loadings	Eigenvalue	% of Variance	Reliability
Positive		4.849	26.941	0.785
Increased the sense of pride of residents in Shanghai	0.775
Strengthened the sense of national spirit	0.742
Enhanced international identity and reputation of Shanghai	0.709
Gave the opportunity to show the world what China can do	0.707
Brought a sense of belonging and sharing to the community	0.678
Improved the infrastructure in Shanghai	0.661
Enhanced the intercultural interaction/understanding between locals and Expo attendees/tourists	0.649
Enhanced good social and moral values	0.586
Established and upgraded more public facilities (e.g., shopping, leisure, and recreation) available to local residents	0.576
Had long-term promotional benefit of Shanghai’s image	0.554
Improved the overall environment of Shanghai (e.g., better city appearance, better air quality)	0.545
Negative		4.741	26.340	0.911
Caused overcrowdedness in Shanghai during the Expo	0.905
Caused more traffic congestion	0.899
Overused public facilities by event visitors (e.g., bus, metro, subway, restaurant, shops)	0.851
Caused inconvenience/disruption of local residents’ life	0.827
Increased the crime level (e.g., theft, vandalism, robbery, common assaults, or delinquent behavior)	0.767
Caused unfairly distributed resources among cities	0.755
Resulted in negative attitudes between local residents and tourists	0.650

Note: Kaiser-Meyer-Olkin = 0.902, chi-square = 3089.251, df = 153, p < .001.

Results of CFA (Table 3) from Wave 1 and Wave 3 data generally supported the factor structure. However, the model fit using the Wave 2 data showed less-than-satisfactory results. The composite reliabilities of the derived factors were calculated (Table 4). All AVE estimates were greater than their corresponding interfactor squared correlations, indicating good discriminant validity between two factors.

Table 3.

CFA Results for Social Impacts.

	Wave 1	Wave 2		Wave 3
	Individual	Community	Individual	Community	Individual
χ²	224.37***	327.69***	285.51***	268.91***	250.47***
df	134	134	134	134	134
SRMR	0.056	0.093	0.071	0.085	0.075
GFI	0.87	0.80	0.89	0.89	0.89
NFI	0.97	0.84	0.87	0.93	0.94
CFI	1.00	0.90	0.99	1.00	0.99
RMSEA	0.025	0.092	0.025	0.019	0.033

Note: Chi-square values were corrected for non-normality. SRMS = standardized root mean square residual; GFI = goodness of fit; NFI = normed fit index; CFI = comparative fit index; RMSEA = root mean square error of approximation.

***

p < .001.

Table 4.

Composite Reliability of Social Impact Factors.

	Wave 1		Wave 2		Wave 3
	Community	Individual	Community	Individual	Community	Individual
Positive	0.871	0.943	0.839	0.789	0.795	0.895
Negative	0.917	0.905	0.780	0.760	0.912	0.876

Hypotheses testing

Hypotheses 1a and 1b state that residents’ perceived impacts of the Expo at individual and community levels were significantly different. To test these hypotheses, paired t-tests were conducted using the three waves of data individually. All items except one in Wave 3 showed significant differences, thus supporting both hypotheses (Table 5). Respondents generally rated the impact at the community level more favorably than at the individual level.

Table 5.

Perceived Impacts of the Expo at Individual and Community Levels.

	Wave	Community Mean	Individual Mean	t-value
Economic Impacts
There are more business opportunities in Shanghai as a result of the Expo 2010	1	4.47	3.89	12.213***
	2	4.26	3.53	15.349***
	3	4.47	3.95	11.895***
Employment opportunities increased as a result of Expo 2010	1	4.06	3.69	7.881***
	2	3.99	3.27	16.420***
	3	4.18	3.75	10.377***
The Expo 2010 Shanghai China caused the increase in prices of goods and services in Shanghai	1	3.55	2.78	11.132***
	2	3.78	3.12	11.466***
	3	4.13	3.34	12.059***
The Expo 2010 Shanghai China brought substantial financial return to Shanghai government	1	4.34	3.81	10.081***
	2	4.01	3.39	12.607***
	3	4.36	3.94	9.468***
The Expo 2010 Shanghai China increased the income of local people	1	2.92	3.30	−7.094***
	2	3.70	3.28	8.848***
	3	3.78	3.75	.820
Residents’ personal economic burden (e.g., taxes, cost of living) increased as a result of the Expo 2010 Shanghai China	1	3.00	2.52	8.022***
	2	3.76	3.13	12.122***
	3	4.00	3.46	8.614***
The Expo 2010 Shanghai China increased the tourism revenue to Shanghai	1	4.42	3.81	10.750***
	2	4.14	3.35	16.779***
	3	4.55	4.00	11.760***
Social Impacts
Positive	1	4.36	4.02	13.315***
	2	3.91	3.43	21.412***
	3	4.38	4.14	12.493***
Negative	1	2.70	2.41	7.610***
	2	3.62	3.12	16.003***
	3	2.92	2.61	8.976***

5 = strongly agree, 1 = strongly disagree.

5 = very positive, 1 = very negative.

***

p < 0.001.

Hypotheses 2a–2d suggest that among residents, attendees’ and nonattendees’ perceived impacts of the Expo at the individual and community levels were significantly different. To test these hypotheses, independent samples t-tests were performed using the three waves of data separately. It appears that attendees and nonattendees shared very similar views on the positive impacts at both individual and community levels throughout the three waves. Four of the 12 sets of comparisons of social impacts showed significant differences, but these four all happened on negative social impacts. Interestingly, attendees rated the negative social impacts higher at the beginning of the Expo at both community and individual levels; however, nonattendees rated the negative social impacts higher six months after the event (Table 6). Thus, Hypotheses 2a and 2c are partially accepted.

Table 6.

Attendees’ and Nonattendees’ Perceived Social Impact.

Social Impact	Wave	Community Level^a			Individual Level^b
Social Impact	Wave	Attendees Mean	Nonattendees Mean	t Value	Attendees Mean	Nonattendees Mean	t Value
Positive	1	4.37	4.35	0.142	4.01	4.03	−0.184
	2	3.93	3.83	1.855	3.44	3.38	1.302
	3	4.39	4.29	1.872	4.15	4.02	1.772
Negative	1	2.95	2.65	2.083*	2.63	2.36	2.385*
	2	3.63	3.58	0.703	3.13	3.09	0.822
	3	2.86	3.25	−2.739**	2.57	2.83	−2.194*

Community level: 5 = strongly agree, 1 = strongly disagree;

Individual level: 5 = very positive, 1 = very negative.

p < 0.01; *p < 0.05.

As for economic impacts, only two of the 21 sets of comparisons between attendees and nonattendees at the community and individual levels, respectively, showed significant differences (Table 7). Specifically, at the community level, attendees showed a substantially higher level of agreement to the item “There are more business opportunities in Shanghai as a result of the Expo 2010” than nonattendees (p < 0.01) in Wave 3. In Wave 2, attendees indicated more support to the statement “The Expo 2010 Shanghai China increased the tourism revenue to Shanghai” (p < 0.01). At the individual level, attendees also agreed more that “the Expo 2010 Shanghai China brought substantial financial return to Shanghai government” and “the Expo 2010 Shanghai China increased the tourism revenue to Shanghai” in Wave 2 (both p < 0.05). Thus, Hypotheses 2b and 2d are partially accepted. Nonetheless, overall, the visiting status of respondents did not appear to influence their perceptions of the impacts at both the community and individual levels.

Table 7.

Attendees’ and Nonattendees’ Perceived Economic Impact.

	Wave	Community Level ^a			Individual Level ^b
	Wave	Attendees Mean	Nonattendees Mean	t Value	Attendees Mean	Nonattendees Mean	t Value
There are more business opportunities in Shanghai as a result of the Expo 2010.	1	4.49	4.47	0.228	3.72	3.92	−1.434
	2	4.23	4.34	−1.164	3.50	3.61	−1.172
	3	4.51	4.24	2.649**	3.96	3.86	0.875
Employment opportunities increased as a result of Expo 2010.	1	4.00	4.07	−0.526	3.61	3.71	−0.780
	2	3.97	4.06	−0.986	3.25	3.35	−1.471
	3	4.19	4.08	0.891	3.76	3.67	0.717
The Expo 2010 Shanghai China caused the increase in prices of goods and services in Shanghai.	1	3.61	3.53	0.544	2.74	2.79	−0.356
	2	3.75	3.87	−1.139	3.12	3.11	0.100
	3	4.12	4.18	−0.677	3.36	3.18	0.917
The Expo 2010 Shanghai China brought substantial financial return to Shanghai government.	1	4.28	4.36	−0.625	3.72	3.83	−0.747
	2	4.02	3.96	0.576	3.43	3.27	2.040*
	3	4.37	4.29	0.829	3.95	3.90	0.392
The Expo 2010 Shanghai China increased the income of local people.	1	3.03	2.90	1.019	3.48	3.26	1.709
	2	3.66	3.83	−1.582	3.25	3.35	−1.285
	3	3.77	3.88	−0.760	3.74	3.80	−0.379
Residents’ personal economic burden (e.g., taxes, cost of living) increased as a result of the Expo 2010 Shanghai China.	1	3.02	3.00	0.144	2.51	2.52	−0.124
	2	3.76	3.76	0.048	3.12	3.15	−0.259
	3	3.98	4.12	−1.183	3.47	3.43	0.200
The Expo 2010 Shanghai China increased the tourism revenue to Shanghai.	1	4.52	4.40	1.552	3.69	3.83	−1.112
	2	4.21	3.93	2.830**	3.39	3.23	1.980*
	3	4.56	4.49	0.804	4.00	4.00	0.000

5 = strongly agree, 1 = strongly disagree.

5 = very positive, 1 = very negative.

p < 0.01; *p < 0.05.

Hypotheses 3a–3d indicate that residents’ perceived impacts of the Expo at individual and community levels differed significantly at the beginning of, end of, and 6 months after the Expo. To test these hypotheses, ANOVAs were conducted to compare means of the three waves of responses on economic impact items and social impact factors separately.

Tables 8 and 9 show differences in social impact and economic impact, respectively, in support of hypotheses 3a–3d. For most items, respondents reported similar perceptions at the beginning of and six months after the Expo (their six-months-after perceptions seemed to be even more positive); however, their perceptions at the end of the Expo were the most negative when they rated positive impacts lower and negative impacts higher. Ratings of the last three economic impact items (i.e., “The Expo 2010 Shanghai China caused the increase in prices of goods and services in Shanghai”; “The Expo 2010 Shanghai China increased the income of local people”; “Residents’ personal economic burden [e.g., taxes, cost of living] increased as a result of the Expo 2010 Shanghai China”) deviated from the above observation, which will be discussed in the next section. The authors prepared an extra figure to visualize changes over the three waves (Figure 1).

Table 8.

Differences in Social Impact among the Three Waves.

	Wave 1	Wave 2	Wave 3	F Value	df
Community level
Positive impact	4.36a	3.91a,b	4.38b	138.491***	2
Negative impact	2.70a,b	3.62a,c	2.92b,c	100.542***	2
Individual level
Positive impact	4.02a,b	3.43a,c	4.14b,c	194.529***	2
Negative impact	2.41a,c	3.12a,b	2.61b,c	95.438***	2

Note: Community level: 5 = strongly agree, 1 = strongly disagree; individual level: 5 = very positive, 1 = very negative. Numbers followed by the same letter were significantly different.

***

p < .001.

Table 9.

Differences in Economic Impact among the Three Waves.

	Wave 1	Wave 2	Wave 3	F Value	df
Community level
There are more business opportunities in Shanghai as a result of the Expo 2010.	4.47a	4.26a,b	4.47b	10.780***	2
The Expo increased the tourism revenues to Shanghai.	4.42a,b	4.14a,c	4.55b,c	36.173***	2
The Expo 2010 Shanghai China brought substantial financial return to Shanghai government.	4.34a	4.01a,b	4.36b	24.276***	2
Employment opportunities increased in Shanghai as a result of the Expo 2010.	4.06	3.99a	4.18a	5.439**	2
The Expo 2010 Shanghai China caused the increase in prices of goods and services in Shanghai.	3.55a,b	3.78a,c	4.13b,c	44.613***	2
Residents’ personal economic burden (e.g., taxes, cost of living) increased as a result of the Expo 2010 Shanghai China.	3.00a,b	3.76a,c	4.00b,c	109.172***	2
The Expo 2010 Shanghai China increased the income of local people.	2.92a,b	3.70a	3.78b	90.881***	2
There are more business opportunities in Shanghai as a result of the Expo 2010.	3.89a,b	3.53a	3.95b	25.302***	2
The Expo increased the tourism revenues to Shanghai.	3.81a,b	3.35a,c	4.00b,c	56.996***	2
The Expo 2010 Shanghai China brought substantial financial return to Shanghai government.	3.81a	3.39a,b	3.94b	40.476***	2
Employment opportunities increased in Shanghai as a result of the Expo 2010.	3.69a	3.27a,b	3.75b	41.374***	2
The Expo 2010 Shanghai China caused the increase in prices of goods and services in Shanghai.	2.78a,b	3.12a,c	3.34b,c	27.033***	2
Residents’ personal economic burden (e.g., taxes, cost of living) increased as a result of the Expo 2010 Shanghai China.	2.52a,b	3.13a,c	3.46b,c	90.981***	2
The Expo 2010 Shanghai China increased the income of local people.	3.30a	3.28b	3.75a,b	35.791***	2

Note: Community level: 5 = strongly agree, 1 = strongly disagree; individual level: 5 = very positive, 1 = very negative. Numbers followed by the same letter were significantly different.

***

p < 0.001; **p < 0.01.

Figure 1.

A Visual Summary of the Perceived Socioeconomic Impacts among the Three Waves.

Discussion

Because of their length and magnitude, World Expos are unique to study in that they arguably possess hybrid features of both permanent tourism projects and short events. This study tracked residents’ perceptions of the 2010 Shanghai Expo’s social and economic impacts over a one-year period. The three-wave survey approach including a postevent assessment allowed the authors to systematically examine the issue. Conceptually, the study contributed to the literature by integrating two theoretical perspectives in understanding local residents’ perceived impacts of a mega-event.

It was found that Shanghai residents’ perceived impacts at the community level were more positive than at the individual level; residents reported similar perceptions at the beginning of and six months after the Expo (with the latter being more positive), but their perceptions at the end of the Expo were the most negative; finally, residents who attended or did not attend the Expo held similar attitudes toward the event, albeit attendees did demonstrate more favorable attitudes on several aspects.

One way to explain these differences is that the two theoretical mechanisms (i.e., SRT and SET) were working under different circumstances. Specifically, when asked about Expo impacts at the community level, which is somewhat abstract and unfamiliar, respondents used social representations as a referential framework to form their opinions. On the contrary, respondents were likely to go through a SET-based calculation to answer questions regarding event impacts at the individual level. When the Expo just began (Wave 1), few residents had firsthand experiences with the event; their perceptions were largely collectively determined, which could be strongly affected by government propaganda or media. This happened again six months after the Expo (Wave 3), when personal feelings faded and memories about the events were selectively retained. In both cases, SRT seemed to have played a more active role in respondents’ judgment. On the other hand, at the end of the Expo, when hosts’ involvement with the event was likely at its peak, SET-based thoughts perhaps dominated the thinking process. Finally, Expo attendees were more likely to go through a SET-based thinking process than were nonattendees because of a higher level of involvement and familiarity. Overall, this study provides some support to the idea that SET and SRT can complement each other and are applicable in different contexts.

Some observations were made in this study. For instance, Shanghai residents were found to view the Expo favorably, mainly because it brought benefits to the community rather than to themselves. This is consistent with Meng and Li’s (2011b) finding on Beijing residents’ attitudes toward the Beijing Olympics. Readers are reminded to interpret these findings in the Chinese cultural context, against the backdrop of China’s current socio-economic development. As indicated, one limitation of SET is its assumption of individualist economic calculation, which may not work well in collectivist cultures like China where sacrificing individual interest for a common cause is strongly encouraged. In this case, Shanghai residents seem to have attached symbolic meaning to hosting the World Expo, and connected (or elevated) the success of this event to the country’s national spirit and their community pride (Meng and Li 2011a; Zhou and Ap 2009).

Findings from the three-wave comparisons suggest that Shanghai residents held fairly positive perceptions at the beginning of the Expo, which were later replaced by more negative ones at the end of the event. However, six months after the event, their perceptions became even more positive than the initial level. Findings during the event (Wave 1 and Wave 2) are consistent with Doxey’s (1975) classic “Irridex” model, which asserts that residents’ attitudes toward tourism generally go through euphoria, apathy, irritation, and usually end with antagonism. Notably, many mega-event impact studies reported favorable attitudes among residents. Could this be a result of their short duration, that is, mega-events such as Olympics and FIFA World Cup are not long enough for residents’ negative feelings to accumulate and unfold? Or, probably host community of short events actually goes through similar attitude evolvement to what this paper reported, but at a much faster pace and in a more subtle manner? If so, residents’ negative feelings might peak at some point during the event, but past research using one-shot survey—depending on the survey timing—either could not detect such negative attitudes at all, or, coincidentally captured negative impacts only. Thus, tourism impact researchers are recommended to adopt a longitudinal approach and be more cautious about research timing. More broadly, this study attempts to reemphasize the role of time in theory building (Mitchell and James 2001; Zaheer, Albert, and Zaheer 1999).

This study shows that the Shanghai public became more favorably inclined six months after the Expo. However, unlike most tourism development projects, events are by definition short-lived. Residents’ postevent attitudes toward events are largely based on memory, which can be skewed. Further, it appears Shanghai residents’ social representations might have been at work here—their appreciation of the event improved six months after the Expo when their memory about temporary inconveniences faded away while the fond recollection about the event was occasionally reinforced by the media.

It is puzzling that the ratings of three economic impact items increased in value in the three waves—respondents increasingly agreed that the Expo did cause higher prices, greater economic burden, and paradoxically, more income. Notably, all three items were more related to respondents’ financial well-being, whereas the other four economic items were more about the government and the city. Findings regarding price and economic burden probably reflect the general economic condition of the time of data collection, which affected the responses when the inflation rate was high and people experienced more economic burden as time progressed. As for income, it appears respondents did not necessarily receive higher income themselves but did believe that other Shanghai residents could have financially benefited from the Expo.

The poor fit of the social impact measurement structure in Wave 2 suggest that after living with the Expo for six months, residents’ perceptions of the event changed. The differences were also evident in results of hypothesis 3 tests. Not only did their assessment of the impact items change, their ways of examining the social impact may have changed as well, and hence a different construct structure. Interestingly, results of Wave 3 were similar to those of Wave 1, both in item means and factor structure. This could reflect residents’ rapid memory decay in terms of assessing the impact of mega-events.

These findings are by no means conclusive; however, they offer numerous opportunities for future exploration. For example, is there a universal event impact scale regardless of measurement time point, or shall different measurements be used at different time points? How quickly do residents forget about the impacts from events? What factors cause the differences in measurement construct and memory decay?

Another finding worthy of discussion is that among local residents, attendees and nonattendees of the Shanghai Expo did not differ substantially in their perceptions, although attendees’ attitudes were more favorable in certain areas. The scale and length of the event, the collectivist Chinese culture, and the extent of government propaganda could all have played a role here. A World Expo represents the “in your face” type of tourism (Gursoy and Kendall 2006) that intrudes into residents’ life space; even nonattendees cannot avoid its impacts. Further, Shanghai government’s communication via mass media seemed to be successful in bringing all residents onboard and cultivating a sense of community pride in hosting this mega-event. Overall, although in this case the distinction between attendees and nonattendees among locals was minimal, future research on smaller events or events hosted in other countries/cultures may reveal different findings.

Interestingly, the findings showed that attendees rated some of the negative social impact higher at the beginning of the Expo whereas nonattendees rated the negative social impacts higher six months after the event. This seemed to reflect different levels of involvement—attendees were more engaged in the event, and more concerned about its negative impacts initially. However, their presumably favorable participation experience could very well have offset the negative perception. On the contrary, nonattendees were less concerned about the event at the beginning but became more aware of the negative impacts at a later point when inconveniences were caused.

Managerial Implications

Findings of this study suggest that organizers of the Shanghai Expo did a good job in fostering community pride and support, even though locals did not necessarily receive substantial direct rewards from the event. Due to the length and magnitude of this mega-event, local residents, unsurprisingly, did express some frustration at the end of the Expo, but still later resumed a more positive impression. Therefore, the authors recommend that future event organizers focus more on monitoring and communicating event-related “personal gains” to the local community during the event. To sustain residents’ support throughout the event, organizers may consider providing different incentives at different stages of the event to nurturing the sense of personal benefits.

The BIE, event hosts and organizers, and local governments are also advised to undertake more public relations work to introduce possible impacts to residents early in the game. It is crucial that residents understand both the benefits and costs to hosting these events because the success of any event strategy ultimately depends on stakeholder awareness of these issues and support for these mega-events. Resident attitudes for the Shanghai Expo would be considered very positive from an event organizer’s point of view. However, the long-term challenge for event organizers and government officials is maintaining a consistent level of support throughout its duration because residents are not homogeneous and their attitudes tend to shift easily. Thus, both top–down and bottom–up planning approaches are suggested to government officials and event organizers who are ultimately responsible for meeting the needs of all residents. Meanwhile, in China’s case, concern has been raised that hosting mega-events is becoming an “excuse” for local government officials to secure political and economic gains (Bao and Li 2012). Thus, how to translate grandiose visions into tangible benefits and effectively communicate them to the local community is not simply a public relations issue, but a critical strategic choice with profound implications to the host city development.

Limitations and Future Research

Several limitations of this study deserve attention. For instance, it used three proportional stratified random samples at three time points, whereas ideally, a panel study with the same group would generate more convincing results. Statistically, that would allow researchers to use more sophisticated techniques, such as latent growth modeling, which are more sensitive in examining change over time (Mitchell and James 2001). Meanwhile, findings of the present study are not sufficient to answer the “why” question yet, because the very nature of the survey method limits its capability in detecting underlying factors behind the judgment and reasoning process of respondents. Future research taking qualitative approaches may address such problems.

Another issue is the unconventional approach the authors used in testing theories—instead of direct verification or falsification, the authors attempted to integrate them and explore their convergence. The results should be interpreted with caution as the hypothesis-testing process does not necessarily provide expressive support to SET or SRT— because the hypotheses were not directly derived from the two theories. The fact that SRT does not generate immediately testable hypotheses (Moscovici 1988) forced the authors to rely on their “disciplined imagination” (Weick 1989) in the theorizing process. The conclusions drawn are hence less straightforward than desired.

Conclusions

A contribution of this study is the authors’ attempt to navigate the boundary of two widely used theories when examining mega-event impacts. Findings appear to support the authors’ reasoning that SET tends to work when respondents form perceptions on things directly related to their own life, and when they are more knowledgeable about or involved in the event. Alternatively, respondents’ perceptions on more abstract matters in which they lack direct knowledge/experiences tend to derive from their social representations. One may argue that extant literature on resident attitudes has largely focused on finding theories to describe and explain the attitude formation process, whereas this article attempts to sensitize readers to the theory boundary issue (Hubbard and Linsay 2002; Whetten 1989).

Fifteen years ago, Faulkner and Tideswell (1997) pointed out that tourism impact studies suffered from two important limitations: “First, existing theory is fragmented and needs to be integrated into a more general framework that can guide empirical investigation towards a cumulative development of knowledge. Secondly . . . the theory developed so far has remained little more than a series of assertions which have not been empirically tested in a systematic way.” More recently, Vargas-Sánchez, Porras-Bueno, and Plaza-Mejía (2011, p. 464) also suggested that several theories used in explaining resident attitudes toward tourism development “must be considered complementary and interrelated, and . . . have to be analyzed holistically.” Following this line of thought, the present study contributed to the literature by integrating two theoretical perspectives in making sense of residents’ perception changes and differences over the life cycle of a mega-event. This effort resonates with a growing body of literature advocating the utility of integrated knowledge bases and broadened research lenses (Chaiken and Trope 1999; Hubbard and Linsay 2002; Shavitt 2011; Wyer and Shavitt 2005). After all, it is hardly realistic to expect one single theory to reveal the categorical truth of a complex social phenomenon (Pham 2013).

Footnotes

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: The authors gratefully acknowledge funding from the School of Hotel and Tourism Management, The Hong Kong Polytechnic University, P. R. China (G-U835); The Centre for Tourism, Sport and Services Research, Griffith Business School, Griffith University; and the College of Hospitality, Retail, and Sport Management, University of South Carolina.

Author Biographies

Xiang (Robert) Li, PhD, is an associate professor in the School of Hotel, Restaurant, and Tourism Management at University of South Carolina in Columbia, SC, USA. His research interests include tourist behavior and psychology, international destination marketing, and tourism knowledge development.

Cathy H.C. Hsu is a professor in the School of Hotel and Tourism Management at The Hong Kong Polytechnic University. Her research foci have been tourism destination marketing, Chinese outbound tourist behaviors, hotel branding, service quality, and the economic and social impacts of casino gaming.

Laura J. Lawton is Associate Professor and Deputy Head of the Department of Tourism, Sport and Hotel Management at Griffith University, Gold Coast Campus, Australia. Her specialties include resident perceptions of tourism, special events, cruise ship tourism, and sustainable tourism. Laura is the Deputy Chair and a Board Member and an accreditation auditor for the International Centre of Excellence in Tourism and Hospitality Education (THE-ICE).

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