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PRE- AND POST-CASINO IMPACT OF RESIDENTS’ PERCEPTION
www.elsevier.com/locate/atoures
Annals of Tourism Research, Vol. 30, No. 4, pp. 868–885, 2003 2003 Elsevier Ltd. All rights reserved Printed in Great Britain 0160-7383/$30.00
doi:10.1016/S0160-7383(03)00060-4
PRE- AND POST-CASINO IMPACT OF RESIDENTS’ PERCEPTION Choong-Ki Lee Kyunghee University, South Korea Ki-Joon Back Kansas State University, USA Abstract: While most of the previous research on residents’ perceptions was conducted in the form of snapshots taken at a particular time, or after tourism development, this paper aims to explore any significant differences in these perceptions between pre- and post-development of casinos. The survey for this study was developed to examine the underlying relationships among impact, benefit, and support variables based on social exchange theory, using a structural equation model. The results show that positive economic impact was most significant in determining the benefit level, which was further enhanced after the casino opened. Respondents perceived positive social impacts to be most significant both before and after casino development. Keywords: casino, residents’ perception, structural equation modeling. 2003 Elsevier Ltd. All rights reserved. Re´sume´: Perceptions des habitants avant et apre`s l’ouverture d’un casino. En contraste avec la plupart des recherches ante´rieures sur les perceptions des habitants, qui repre´sentent des moments isole´s ou une pe´riode apre`s le de´veloppement du tourisme, le but du pre´sent article est de de´terminer s’il y a des diffe´rences significatives entre les perceptions avant et apre`s le de´veloppement d’un casino. On a formule´ une enqueˆte pour examiner les relations sous-jacentes entre l’impact, les be´ne´fices et les facteurs contribuants en se basant sur la the´orie de l’e´change sociale et en utilisant un mode`le d’e´quation structurelle. Les re´sultats montrent qu’un impact e´conomique positif e´tait l’e´le´ment le plus significatif pour de´terminer le niveau de be´ne´fices, qui e´tait ame´liore´ apre`s l’ouverture du casino. Les sonde´s trouvaient, avant et apre`s le de´veloppement du casino, que les impacts sociaux positifs e´taient les plus significatifs. Mots-cle´s: casino, perception des habitants, modelage d’e´quation structurelle. 2003 Elsevier Ltd. All rights reserved.
INTRODUCTION Several recent studies on the impacts of gaming legalization have been reported in the literature (Hsu 2000; Long 1996; Perdue, Long and Kang 1995; Roehl 1999). The impacts of legalizing Native-American gaming (Connor 1993), riverboat casinos (Labalme 1994), and land-based casinos (Pizam and Pokela 1985) are well documented, with their foundation stemming mainly from the impact studies of the 70s (Hsu 2000). Most research has focused on residents’ perceptions of
Choong-Ki Lee is Associate Professor in the College of Hotel & Tourism at Kyunghee University (Dongdaemun-ku, Seoul Korea. Email). His research interests include casino policy and tourism demand. Ki-Joon Back is Assistant Professor in the Department of Hotel, Restaurant, Institution Management and Dietetics at Kansas State University. His research areas are brand loyalty and residents’ perceptions toward casinos. 868
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the social, economic, and environmental impacts of gaming on their communities. For instance, researchers have considered the impacts of gaming on crime rate, alcoholism, drug abuse, divorce rate, employment opportunities, tourist spending, tax revenues, disposable income, noise level, crowding, and traffic congestion. According to Carmichael, Peppard and Boudrea (1996), residents’ attitudes are important because they are rarely expressed in the political and development decisionmaking process. Their study results indicated that there was a growing awareness by local residents of both the negative impacts of rapid development and the positive employment benefits of casino development. Roehl (1999) compared Nevada residents’ attitudes toward the impacts of gaming in 1975 and 1992. The research found that the majority of respondents agreed that legalized casinos created more jobs, generated more money for the government, and allowed people to gamble more than they could afford to. However, the 1992 sample indicated that they were less likely to agree with the items “more money to run the government.” Long (1996) examined residents’ attitudes toward casino development in South Dakota and Colorado. Residents generally perceived a positive impact on employment, but a negative one in terms of traffic congestion, crowding, and crimes caused by gambling. The majority of respondents did not recommend legalized gambling to other communities. Attention to gaming development has also evolved into the exploration of the host community’s quality of life issues (Perdue et al 1995). Giacopassi, Nichols and Stitt (1999) worked with policy makers in seven communities that were new riverboat casino jurisdictions. Results showed that the majority of respondents favored the casino in the community and believed that it enhanced the quality of life by providing positive impacts on the economy. The perceptions or behaviors of residents can be explained by applying the social exchange theory, which attempts to understand and predict the behavior of individuals in an interactive situation (Ap 1990). Based on the literature, residents who perceive personal benefit from casino development will support and express positive attitudes toward it. Perdue, Long and Kang (1999) supported the social exchange theory in that such residents were more likely to be positive in assessing the quality of life. They also found that personal gains were strongly correlated with support for gambling and its positive impacts, such as jobs and recreation opportunities. The results revealed that the sociodemographic characteristics of respondents were not related to the perceived impact of gambling when controlling for personal benefits. Further, the support was a function of personal benefits, future of the community, positive and negative impacts of gambling, and quality of contact with gamblers. Korean Gaming Impact Although 13 casinos have been in operation at international hotels in Korea since the late 60s and early 70s, only foreigners are allowed
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to enter those establishments. Walker-Hill Hotel and Casino, the largest casino in Korea, is operating in the metropolitan Seoul, while eight are located in Cheju Island, experiencing oversupply due to diminished market demand. In 2001, some 626,000 tourists visited Korean casinos and their receipts totaled approximately $296 million (Korean Casino Association 2002). The survey results show that the Japanese represented 70%, followed by Mainland Chinese, the Taiwanese and Hong Kong Chinese (23%), and Americans (5%) (Lee and Kwon 1997). However, the government prohibited Korean people from entering the casinos because it worried about predominance of social issues, such as problem gambling, financial crisis, usury, and bankruptcy in the Confucian society. This might contribute to the fact that gaming impact studies in Korea are rare. But in December 1995, the Korean government legalized gaming in the run-down former coal mining areas (Chongsun, Taeback, Samcheok and Youngwol) of Kangwon province for domestic customers. These towns had once experienced a “gold rush” at a time in history when coal was used as a major source of energy for industries and households. As coal was replaced by new energy sources such as oil and gas, the economy of the communities rapidly declined. The Korean government pursued various economic revitalization policies, to no avail. After the four communities repeatedly demonstrated for legalizing gaming for domestic customers in order to revitalize the dilapidated mining towns, the government first legalized a casino in one of the four communities (Chongsun), and the Kangwon Land Casino opened for domestic customers in October 2000. This property has 30 table games and 480 slot machines and is attached to a deluxe hotel with 199 rooms. Prior to opening the property, the government expected to experience numerous positive economic impacts, including increased employment rate, income, sales revenue in local businesses, and so on. In 2002, 918,698 people visited the Kangwon Land Casino (2,517 players per day as compared to a planned capacity of 700 people) and generated $397 million (daily revenue of $1.1 million) (Kangwon Land Casino 2003). The casino hired 862 people from the communities, representing 37.1% of total employees (2,323) and paid $156 million for national and local taxes, including funds for communities in 2002 (Kangwon Land Casino 2003). Sales volumes have been increased in lodging facilities and gas stations by approximately 50%, restaurants by 20 to 30%, and railroad passengers by 300% (Kangwon Province 2000). Despite the many positive impacts of the casino, it began to cause serious problems, such as gambling addiction, usury, and bankruptcy. Local residents were reported to have lost tens of millions of won (US$1ⱌ1,200 won) within a few days of its opening (Soh 2000). A considerable number of patrons with “gambling mania” were obsessed with the possibility of hitting the jackpot or table games and tended to remain several days at the casino. “I came here five times and lost a total of 50 million won. I think more than 300 people are staying on a long-term basis”, said a man who stayed about 4 to 5 days whenever he visited the property (Soh 2000:3). A survey result indicated
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that typical players appeared to lose an average of 4.5 million won per visit (Choe 2000). The number of pawnshops rapidly increased just after the casino’s opening, and financial crises and problem gamblers were frequently reported by mass media. However, much of the qualitative data, including residents’ attitude toward the casino and social perspectives, have not been well reported. MEASUREMENT PROCESS AND IMPACT ANALYSES Homogeneous findings about residents’ perceptions of the different types of casinos, their location, and their history are well described. However, Mason and Cheyne stressed that “there are few studies on the perceived impacts of tourism either prior to any development or when it is not seen to be a significant economic area of activity for a region…studies have been in the form of snapshots taken at a particular time in a particular location” (2000:392). Therefore, this study aims to explore the changing attitudes of residents towards the preand post-development of casinos, using a structural equation model. Specifically, this study was conducted to explore the underlying factors affecting residents’ perceptions of casino development in terms of social, economic, and environmental impacts by constructing measurement scales. The study was also designed to investigate the significant differences in residents’ perceptions between pre- and post-development of casinos. Further, it was meant to examine the underlying relationships among impact, benefit, and support variables based on the social exchange theory, using a structural equation model. Characteristics of Respondents Two major casino communities—designated by a special law as rundown mining areas—were chosen for survey research. One consisted of two towns, Kohan and Sabuk, with a total population of approximately 16,000 in Chongsun County where the casino is located. This community is considered to be a direct impact area of it. Another community, Taeback City, is larger, with a population of approximately 60,000. It has a relatively well-developed operations, including lodging, restaurants, and other entertainment facilities, but is a 30-minute drive from the casino. Thus, this community is considered to be an indirect impact area from casino development. The data for this study were collected in two different time frames, through pre- and post-surveys. The former sample was taken at the end of June, 2000, before the casino opened. The number of samples was proportionately allocated based on occupation, using the official statistics of Chongsun County and Taeback City, Kangwon Province. A self-administered questionnaire was given to those who preferred to complete it by themselves, otherwise completed by the field researchers via personal interview. Respondents of at least 18 years of age were asked to participate in the survey and one person was chosen for the sample in cases of there being a group of people. To increase the response rate, small gifts were offered to those completing the ques-
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tionnaire. Respondents’ names and phone numbers were secured so that the same individuals could be used in the post-survey. A total of 517 usable questionnaires were ultimately collected during the prestudy. In December 2000, the post-survey was administered to those who had responded to the initial one after the Kangwon Land Casino opened. Each researcher was posted to the same site as in the presurvey and was given information on the respondent’s name, phone number, and work place as collected earlier. First, respondents were asked whether they had participated in the pre-survey. If they had, then the post-survey was conducted. Not all subjects were available, because they were out of town, moved away, quit their jobs, or closed businesses. On this occasion, a total of 404 usable questionnaires were finally collected, fewer than the initial sampling. Table 1 presents demographic characteristics of the respondents. The proportion of male participants (53.7%) was slightly higher than that of the female (46.3%). The majority were married (72.8%), aged 30 to 49 (63.6%), high school graduates (51.2%), and long-time residents of the city (21 to 40 years: 49.1%). The majority earned less than 2 million won (approximately $1,667) each month. Respondents who owned their houses totaled 50.2%, but those who owned land constituted only 15.8%. Of the respondents, 52.2% stated that they were born in the casino community. Measurement of Constructs A preliminary list of measurement items was initially generated from a review of the literature pertaining to residents’ perceptions toward tourism and casino impacts (Pizam 1978; Pizam and Pokela 1985; Liu and Var 1986; King, Pizam and Milman 1993; McCool and Martin 1994; Perdue et al 1990, 1995, 1999; Carmichael et al 1996; Long 1996; Jurowski, Uysal and Williams 1997; Lindberg and Johnson 1997). These items were then screened by scholars and community leaders of the casino town. They were asked to clarify these items, and comment whether the items were likely to be appropriate for evaluating residents’ attitudes toward the casino. After their comments, a pretest composed of the items was conducted with graduate students at one of the Korean Universities and with gaming community residents to further refine the list of items. Validity of dimensionality and intercorrelation was examined by factor analysis. Although it costs more than a mail survey, a direct face-to-face survey method was employed because of the expected improved response rate. Mail surveys have been commonly employed in the United States, but this technique appears to be inefficient in Korea due to a lower response rate. The theoretical model was developed based on the social exchange theory. As mentioned earlier, many researchers have utilized this conceptual frame to integrate factors influencing residents’ reactions to casino development. The social exchange theory assumes that their perceptions are affected by how they perceive the exchange they believe they are making (Gursoy, Jurowski and Uysal 2002). Jurowski
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Table 1. Demographic Characteristics of Respondents Characteristics Gender Male Female Age Less than 20 20–29 30–49 50 and higher Education Less than middle school High school 2-year college university or higher Monthly Income (1,200 won = US$1) less that 1 million won 1.00–1.99 million won 2.00–3.99 million won 4 million won or higher Length of Residence Less than 1 year 1–10 years 11–20 years 21–40 years 41 years or longer Marital Status Single Married Children in Household? Yes No Home Ownership Own Rent Land Ownership Own No Birth Place Here? Yes No
Percentage (N = 404)
53.7 46.3 3.0 25.7 63.6 7.7 11.9 51.2 16.1 20.8
40.6 41.3 16.8 1.3 1.2 20.1 24.8 49.1 4.7 27.2 72.8 48.5 51.5 50.2 49.8 15.8 84.2 52.2 47.8
et al (1997) state that residents’ support for tourism development should be considered as their willingness to take an exchange based on the social exchange theory. The model postulates that exogenous variables have both direct and indirect effects on benefit and support. The theoretical model tested, as shown in Figure 1, involved eight constructs: negative social, negative environmental, negative economic, positive social, positive
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Figure 1. Theoretical Model (1A) of Residents’ Perception toward a Casino
environmental, and positive economic factors, as well as benefit, and support. The negative social factor was composed of nine items: occurrences of gambling addicts, speculative gambling spirit, bankruptcy, destruction of family, prostitution, divorce, alcoholism, crime, and political corruption. The negative environmental factor was composed of six items: crowding due to tourists, traffic congestion, quantity of litter, noise level, water pollution, and destruction of natural environment. The negative economic factor comprised three items: cost of living, increased tax burden, and leakage of casino revenues. Further, positive social factor was composed of four items: quality of life, consolidation of community spirit, improvement of educational environment, and pride of local residents. The positive environmental factor was made up of two items: preservation of historic sites and natural beauty. The positive economic factor was composed of six items: investment and business, employment opportunity, tourist spending, tax revenue, public utilities and infrastructure, and standard of living. The benefit factor was made up of two items: How casino development provides benefits to the respondent and to the local community. The support factor was composed of five items: “I believe the future of our city is bright due to the casino industry”; “I am proud that I live in this city”; “the casino industry makes this city a better place to live”; “I support development of the casino”; and “the development of casino is the right choice for this city”. Residents’ perceptions were measured on a 5-point Likert-type scale (1 = strongly disagree, 3 = neutral, and
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5 = strongly agree). Respondents were asked to rate how much they agreed with each item on the scale. Reliability, Validity Tests and Confirmatory Factor Analysis A reliability test was used to assess the consistency in measuring the result. As Sekaran (1992) suggested, the coefficient alpha is the most popular measure of reliability for a multi-item scale. It was used to assess the internal homogeneity existing among the items in this study. The coefficient alpha estimates for the multi-item scales used in this study are presented in Table 2. All alpha coefficients for the data exceed or are close to the minimum standard for reliability of 0.7 recommended by Nunnally and Bernstein (1994) for basic research. Thus, the results indicate that these multiple measures are highly reliable for measuring each construct. Content validity ensures that the measure includes an adequate and representative set of items that would describe the concept. The lists of attributes used to measure negative/positive social, environmental, and economic factors, as well as benefits and supports, were selected after an extensive literature review, after interviews with tourism academics in the field of tourism impacts, and after interviews with community leaders of the casino town. All items were pilot-tested and respondents were asked to evaluate the appropriateness of the measuring instruments. It was evident that these research procedures ensured high content validity of the measurement instrument. Using LISREL 8.5, a maximum likelihood confirmatory factor analysis was undertaken to compare an 8-factor model with a different number of factor model and to assess the overall fit of the model. The 8factor model is composed of negative social, negative environmental, negative economic, positive social, positive environmental, and positive economic factors, as well as benefit and support. As shown in Table 2, the results indicated a good fit for the 8-factor model, χ2(597) = 1684.83; p = 0.00, χ2 / df = 2.82, RMSEA = .06, CFI = .91, NNFI = .90, for testing the model. Factor loadings for both pre- and post-survey data are listed in Table 2. Paired t-test between Pre- and Post-Perceptions of Residents Results of paired t-tests indicated that residents’ perceptions were significantly different before and after casino development, as shown in Table 3. Mean values of most variables were found to be lower in the post- than in the pre-survey. The results indicate that residents perceived all negative and positive impact factors as less strong after the casino opened than before. However, residents held stronger perceptions of some types of negative social impacts, such as gambling addiction, encouraging speculative gambling spirits, increasing bankruptcy rates, and destructive effects on the family, after casino development. Other types of negative impact factors scored significantly lower in the post- than in the pre-survey. For negative environmental impact
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Table 2. Results of Confirmatory Factor Analysis: Pre-Data and Post-Data Construct
Internal Consistencya
Negative Social 0.96
Item
Factor Loadings
Pre
Post
Gambling addicts Speculative gambling spirit Bankruptcy Destruction of family Prostitution Divorce Alcoholism Crime Political corruption
0.85 0.81 0.87 0.87 0.87 0.83 0.91 0.93 0.87
0.78 0.82 0.81 0.82 0.85 0.86 0.81 0.85 0.80
Negative Environmental
0.92
Crowding Traffic congestion Quantity of litter Noise level Water pollution Destruction of natural environ.
0.57 0.77 0.91 0.92 0.84 0.82
0.52 0.70 0.84 0.85 0.84 0.75
Negative Economic
0.68
Costs of living Tax burden Leakage of casino revenue
0.80 0.72 0.38
0.78 0.74 0.34
Positive Social
0.87
Quality of life Community spirit Educational environment Pride of local residents
0.53 0.64 0.80 0.50
0.65 0.73 0.85 0.69
Positive Environmental
0.68
Preservation of historic sites 0.73 Preservation of natural beauty 0.56
0.74 0.50
Positive Economic
0.72
Investment and business Employment opportunity Tourist spending Tax revenue Public utilities/infrastructure Standard of living
0.73 0.68 0.66 0.61 0.67 0.48
0.70 0.65 0.67 0.70 0.70 0.43
Benefits
0.75
Personal benefit Community benefit
0.67 0.88
0.71 0.85
Supports
0.82
Bright future Pride Better place to live Support Right choice for the city
0.81 0.77 0.43 0.82 0.72
0.83 0.80 0.72 0.85 0.81
Fit indices: χ 2(597) = 1684.83; p = 0.00, χ 2 / df = 2.82, RMSEA = .06, CFI = .91, NNFI = .90.
a
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factors, residents perceived that environmental concerns such as crowdedness, traffic problems, and noise levels were not really as bad as they had been thought earlier. Their scores were also lower regarding negative economic impact factors, indicating that they did not experience those economic problems as much as they had expected. On the other hand, residents perceived fewer positive impacts in the post-survey than were exhibited before. First, they scored somewhat higher than neutral about quality of life (mean = 3.11) and pride of local residents (mean = 3.19) during the pre-survey. During the followup sampling, they revealed more disagreement (mean = 2.34 and 2.55, respectively). Second, respondents also scored lower for positive environmental factors with regard to casino development. They perceived that it did not help preserve historic sites and natural beauty of the community. Third, they favored casino development when expressing concern about positive economic impact factors during the pre-survey (mean range from 3.42 to 4.00). Afterwards, their scores were much lower on positive economic impact factors. Standard of living scored especially low (mean = 2.32). Benefit factors scored slightly lower, indicating greater disagreement during the post-survey. Respondents also scored support factors slightly lower during the post-survey than before. They had neutral perceptions of benefit and support factors before the casino opening. These perceptions changed to be more negative as they experienced casino development. LISREL Methodology The hypothesized relationships between the factors shown in Figure 1 and the factor models described earlier were examined simultaneously using LISREL 8.5 (Joreskog, Sorbomm, du Toit and du Toit 2001). By using this program, the goodness of fit of the various models was testable and the relative fit of particular pairs of models could be assessed. Three approaches to ascertain consistency of factors were available using this methodology. The first answers the question, “Are there significant relationships among the variables? ” The second answers the question, “Is there a significant difference between the pre- and post- residents’ perceptions toward the Kangwon Land Casino development?” The third approach answers the question, “If there is a significant difference between two groups, what kind of differences occurred in supporting the casino development after the casino was opened?” These approaches were used: the first in models 1A and 1B, the second and third in models 2 and 3, described below. Model 1A, shown in Figure 1, specified the eight factors already described. In this model, direct and indirect paths from negative social, negative environmental, negative economic, positive social, positive environmental, positive economic factors, and benefit to support were specified, as well as direct paths from all those exogenous variables to benefit. To identify the best model for testing, a contrasting one (Model 1B) was created. This was the same as Model 1A with the exception that the direct paths from each exogenous variable to support
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Table 3. Results of Paired T-test between Pre- and Post-Survey Mean Scoresa
Variables
Negative social Occurrence of gambling addicts Speculative gambling spirits Bankruptcy Destruction of family Prostitution Divorce Alcoholism Crime Political corruption Negative Environmental Crowding due to visitors Traffic congestion Quantity of litter Noise level Water pollution Destruction of natural environment Negative Economic Cost of living Increased tax burden Leakage of casino revenues Positive Social Quality of life Consolidation of community spirit Improvement of educational environment Pride of local residents Positive Environmental: Preservation of historic sites Natural beauty Positive Economic Investment and business Employment opportunities Tourist spending Tax revenues Public utilities and infrastructure Standard of living Benefit Benefit to myself Benefit to local residents Support Future is bright due to casino I am proud that I live in this city Casino makes this city a better place to live I support casino development Casino is the right choice for this city
Mean Differenceb
|t-value|
Pre-survey
Post-survey
3.55 3.54 3.51 3.47 3.62 3.45 3.51 3.59 3.47
3.94 3.86 3.79 3.52 3.21 3.09 3.11 3.36 3.27
0.39 0.32 0.28 0.04 ⫺0.41 ⫺0.36 ⫺0.40 ⫺0.23 ⫺0.20
3.89 4.02 4.16 4.02 4.01 4.06
3.13 3.56 3.51 3.36 3.22 3.39
⫺0.76 ⫺0.46 ⫺0.64 ⫺0.66 ⫺0.79 ⫺0.67
11.63∗ 6.76∗ 9.05∗ 9.39∗ 10.88∗ 8.91∗
3.24 3.35 3.78
2.93 3.09 3.54
⫺0.32 ⫺0.26 ⫺0.24
4.67∗ 3.97∗ 3.08∗
3.11 2.76 2.38 3.19
2.34 2.50 1.99 2.55
⫺0.77 ⫺0.26 ⫺0.39 ⫺0.64
11.94∗ 3.63∗ 6.02∗ 9.34∗
2.88 3.72
2.47 3.09
⫺0.41 ⫺0.63
7.10∗ 8.94∗
3.96 3.63 3.94 3.73 4.00 3.42
2.89 3.01 3.17 3.01 3.21 2.32
⫺1.07 ⫺0.62 ⫺0.78 ⫺0.72 ⫺0.79 ⫺1.10
15.73∗ 9.58∗ 11.96∗ 11.29∗ 11.95∗ 19.26∗
3.18 3.27
2.43 2.91
⫺0.75 ⫺0.36
10.41∗ 4.87∗
3.48 2.85 3.15
3.15 2.63 2.66
⫺0.33 ⫺0.22 ⫺0.49
4.42∗ 3.23∗ 6.51∗
3.22 3.33
3.02 3.00
⫺0.20 ⫺0.33
1.93 4.26∗
4.57∗ 3.74∗ 3.41∗ 0.52 5.22∗ 4.79∗ 4.81∗ 2.79∗∗ 2.55∗∗
∗ pⱕ.001; ∗∗ pⱕ.01. a Based on a mean value on a 5 Likert-type scale, where 1=strongly disagree, 3=neutral, and 5=strongly agree. b Mean difference [post-pre survey].
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were eliminated. A well-fitting Model 1A or 1B provides a degree of support for the general theoretical construct used as a basis for the present test for the effects of exogenous variables (impact factors) on endogenous variables (benefit and support) and group analysis between pre- and post-data. The significance of the path coefficients would provide a test for various aspects of the general theoretical model. For assessing the goodness-of-fit, chi-square analysis, Brown and Cudeck’s (1993) root mean square of approximation error (RMSEA), Bentler’s (1990) comparative fit index (CFI), and Bentler and Bonett’s (1980) non-normed fit index (NNFI) were performed. Since the chisquare is almost always significant with large sample sizes, the primary evaluations of fit appropriate for this study are the RMSEA, CFI, and NNFI. Models are generally considered to have good fits if RMSEA is lower than 0.08 and CFI and NNFI are greater than 0.90, although models with indices lower than this may be acceptable in some circumstances (Bentler and Bonett 1980; Browne and Cudek 1993). The two models (1A and 1B) were compared by the chi-square difference test (the differences in its values with degrees of freedom equal to the difference in degree of freedom) and by the difference in the RMSEA, CFI, and NNFI. If the chi-square difference is not significant, the models are judged to be equivalent. The CFI and NNFI are measures of the proportionate improvement fit as one moves from the baseline to the target model. For testing similarity between pre- and post-data, the two groups are not restricted by any of the non-fixed parameters so that they have the same value across groups. This least demanding test of similarity is the same as that in basic model 1A or 1B. If the fit is satisfactory, one moves to the next step by constraining the factor loadings to be equal across the two groups. This was referred to as Model 2. By comparing the goodness of fit between this and the previous one, it could be decided whether the two groups have the same loadings or coefficients from the measurement model. Assuming that constraining the factor loadings to be equal is justified based on the previous two models, the next hierarchy is to constrain all regression weights to be equal across groups. This restricted hierarchical model, Model 3, helps to assess invariance for the general structural equation model with latent variables (Bollen 1989). Once a hierarchy is established, it is possible to assess which degree of invariance best matches the data. Finally, one can measure the invariant effects of exogenous on endogenous variables in the two groups. Model Results Table 4 summarizes the goodness-of-fit results for models 1A, 1B, 2, and 3. The overall fit of Model 1A was good. The chi-square was significant (p < .001), while the CFI and NNFI were about .91 and .90, respectively; whereas RMSEA was .062, implying that the model was appropriate to test further. The overall fit of Model 1B was inferior to that of Model 1A. By constraining the direct effects of exogenous vari-
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Table 4. Summary of Goodness-of-fit Indices Model Model Model Model Model a
1A 1B 2 3
Chi-squarea
df
RMSEA
CFI
NNFI
3758.21 3918.99 3808.20 3970.68
1194 1206 1223 1236
0.062 0.067 0.063 0.067
0.91 0.89 0.91 0.88
0.90 0.88 0.90 0.87
p < .001.
ables (impact factors) on endogenous variable (support) in Model 1B, the statistical indicator of fit (chi-square) and practical indicators of fit (CFI and NNFI) were significantly different from Model 1A (⌬χ 2 = 160.78, ⌬df = 12, p < .001). Thus, it was decided to use Model 1A as a base model for further testing the invariance between pre- and post-data. The overall fit of Model 2 was good. Both the chi-square and practical indicators of fit were significant. For assessing the difference between Model 1A and Model 2, the chi-square and practical indicators were compared, resulting in no significant difference (⌬χ 2 = 49.99, ⌬df = 29). Since Model 2 had equality constraints in factor loadings for pre- and post-data, the result showed that two groups have an invariant structure of manifest variables for indicating latent variables. In other words, the data have similar underlying structures in each factor. As Bollen (1989) suggests, the next step is to assess the invariance of all regression coefficients across groups by adding equality constraints in Model 3. This higher step in the hierarchy assessed the invariance for the general structural equation model with both latent and manifest variables. As Table 4 presents, the overall fit of Model 3 appeared to be good. However, by restricting Model 3 with both factor loadings and path coefficients, the chi-square difference test and practical indicator difference test showed that the fit of Model 3 was significantly worse than that of Model 2 (⌬χ 2 = 162.48, ⌬df = 13, p < .001). Thus, the hierarchical structural equation modeling step stopped in Model 2, suggesting that pre- and post-data had similar underlying structures, but were significantly different with regard to the relationship among latent variables. Figure 2 presents the maximum-likelihood parameter estimates for both pre- and post-data. In the former, support was directly significantly predicted by both negative and positive social, positive environmental, positive economic factors, and benefit (P’s < .01). Support was also indirectly predicted by negative and positive economic factors through benefit (P’s < .01). Among those predictors, all positive factors had significantly direct effects on support, whereas the negative environmental factor had no significant effect on either benefit or support. In the post-data, Figure 2, support was directly significantly predicted
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Figure 2. Standardized Parameter Estimates (Model 2): Pre- and Post Data
by negative and positive social factors, as well as by benefit (P’s < .01). It is interesting to note that the high degree of invariance of the effects of negative and positive social factors and benefit on support was observed in between the data. Direct effects of positive environmental and positive economic factors on support were not found significant in the post-data. For assessing the indirect effect of predictors on support, negative and positive economic impacts were significantly mediated by benefit. Specifically, the negative economic factor was slightly decreased in its effect on benefit (β = ⫺.08, t = 2.53) as compared to pre-data. However, the positive economic factor was significantly increased in its effect on benefit (β = .47, t = 6.12).
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Study Findings The results of the analyses reported here suggest that in many respects, residents showed significantly different perceptions about the casino before and after its development. The underlying structure of each factor was very similar between the pre- and post-data, which suggested that the measurement items had high reliability. Prior to casino development, residents’ support was predicted by negative social factors and positive social, environmental, and economic factors, as well as by benefit. As Figure 2 shows, respondents supported casino development when they perceived receiving personal and community benefits (β = 0.25; t = 5.06) as supported by social exchange theory. They also offered support when concerned about positive social, environmental, and economic factors, such as quality of life, preservation of historic sites and natural beauty, employment opportunity, and standard of living. However, when concerned about negative social factors, such as occurrences of gambling addiction, prostitution, divorce, alcoholism, crime, and political corruption, their support level decreased dramatically (β = ⫺.16, t = 3.01). Negative environmental factors did not predict their support level. It is interesting to note that both negative and positive economic factors had significant effects on their perception about benefit. Although this factor was simply measured by asking for their agreement or disagreement about statements—“the development of casino provides benefits to myself”, “the casino development provides benefits to local residents”—they interpreted these statements as relating more to financial benefits. Thus, both positive and negative economic impacts on benefit were most significant among exogenous variables. Furthermore, positive economic factors had the strongest effect on residents’ support level (β = 0.33, t = 5.87). After the casino development, post-data showed some changes in residents’ support level. Figure 2 exhibits the significant effect of perceptions about benefit on the attitude toward support for casino development. Benefits showed the strongest effect on support (β = 0.66, t = 8.87), whereas positive environmental and economic factors had no significant direct effects on support. Furthermore, positive social factors had less effect on support for post data (β = 0.26, t = 2.76) as compared to pre-data (β = 0.29, t = 4.46). It is interesting to note that positive economic factors had significant effects on support only when mediated by benefit. This result can be interpreted as residents’ exposure to the casino sector that their perceptions of positive environmental and economic factors did not cause them to support it. Rather, they tend to become more supportive when they actually received benefit, specifically economic benefit. The results also showed that those residents’ perceptions about negative social factors were less likely to be predicted by their level of support for the casino after its opening.
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CONCLUSION Given the paucity of research reported in the tourism literature on the related issues, this study sheds light on identifying differences in residents’ perceptions between pre- and post-casino development. The primary intention of this research aimed at exploring residents’ subjective perceptions of changes between pre- and post-development of casinos by measuring levels of economic, social, and environmental impact factors. This study also sought to ascertain how each impact factor determined residents’ benefit and support levels based on the social exchange theory. Confirmatory factor analysis showed six underlying dimensions measuring residents’ perceived impacts of casino development. Results of paired t-tests showed that most of these impact factors scored lower in post-data. Respondents perceived fewer negative impact factors and were less favorable about positive impact factors. Respondents scored low for both benefit and support, indicating that they were not receiving much benefit from casino development and their support level was lower after the casino opened. Results indicated that respondents’ support level was directly influenced by benefit. Although the mean values of benefit and support decreased significantly upon the casino’s opening, the causal relationship became more significant between those two variables. Both positive and negative economic factors showed significant impacts on support when residents actually received benefits. These findings confirmed the study by Perdue et al (1995) that results for residents showed a positive correlation between their support level for casino development and job opportunities and other types of economic impacts. The causal relationship between the positive social impact factor and support level remained the same, whereas negative social impact on support level slightly decreased. These significant economic and social impacts on benefit and support level confirm the social exchange theory. However, environmental impact did not significantly predict either the benefit or support level in post data. In summary, the results showed that positive economic impact was most significant in determining the benefit level, which was further enhanced after the casino opened. Respondents perceived positive social impacts to be most significant in affecting the support level both before and after casino development. The results of the structural equation modeling approach suggest several implications: the social exchange model fits very well in explaining residents’ attitudes toward casino operation in both pre- and post-survey data; policymakers should identify how to provide benefits to local residents so that they can support casino development further; and casino operators and policymakers should make efforts to minimize the negative social impacts, because increase in the level of quality of life or standard of living was not only due to the positive economic impact but also was significantly affected by negative social factors, such as gambling addiction problems. One limitation of the study was that the post-data were collected
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only six months after collection of the pre-survey data. Participants could still remember their responses, causing history bias. The postdata collected for shorter periods of time may also underestimate the impact of casino development if residents have not accessed the casino. In addition, newly-introduced casino development might initially inflate perceptions, due to a short-lived period of residents’ excitement over access to the casino (“novelty effect”), which was not the case in this specific case. Thus, a longitudinal study is strongly recommended to investigate residents’ perceptions of casino development over time, preferably examined on an annual basis. Currently, the authors are conducting a follow-up study for assessing the various impacts of casino development on residents’ quality of life, perception toward benefits, and support level. By conducting a series of longitudinal studies, the difference between resident perceptions and realities will have narrowed even more and researchers should be able to evaluate changes in residents’ attitudes and to identify new positive or negative impacts of the casino. Therefore, policymakers can take appropriate actions to make the community a pleasant place to A live and to improve the quality of life for residents.왎 REFERENCES Ap, J. 1990 Residents’ Perceptions Research on the Social Impacts of Tourism. Annals of Tourism Research 17:610–616. Bentler, P. 1990 Comparative Fit Indexes in Structural Models. Psychological Bulletin 107:238–246. Bentler, P., and D. Bonett 1980 Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychological Bulletin 88:588–606. Bollen, K. 1989 Structural Equations with Latent Variables. New York: Wiley. Browne, M., and R. Cudeck 1993 Alternative Ways of Assessing Model Fit. In Testing Structural Equation Models, K. Bollen and J. Long, eds., pp. 445–455. Newbury Park CA: Sage. Carmichael, B., D. Peppard, and F. Boudreau 1996 Mega-Resort on My Doorstep: Local Resident Attitudes toward Foxwood Casino and Casino Gambling on nearby Indian Reservation Land. Journal of Travel research 34(3):9–16. Choe, Y. 2000 The Korea Herald (November 22):11. Connor, M. 1993 Indian Gaming: Prosperity, Controversy. International Gaming and Wagering Business 14(1):1–45. Giacopassi, D., M. Nichols, and B. Stitt 1999 Assessing the Impacts of Casino Gambling on Crime in Mississippi. American Journal of Criminal Justice 18:117–131. Gursoy, D., C. Jurowski, and M. Uysal 2002 Resident Attitude: A Structural Modeling Approach. Annals of Tourism Research 29(1):79–105. Hsu, C. 2000 Residents’ Support for Legalized Gaming and Perceived Impacts of Riverboat Casinos: Changes in Five Years. Journal of Travel Research 38:390–395. Joreskog, K., D. Sorbom, S. du Toit, and M. du Toit 2001 LISREL 8: New Scientific Features. Chicago: Scientific Software International.
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Jurowski, C., M. Uysal, and D. Williams 1997 A Theoretical Analysis of Host Community Resident Reactions to Tourism. Journal of Travel Research 36(2):3–11. Kangwon Land Casino 2003 Report on Casino Visitors and Revenues. Kangwon Province: Kangwon Land Casino. Kangwon Province 2000 Evaluation Report on Initial Operation of Kangwon Land Casino. Kangwon Province: Internal government material. King, B., A. Pizam, and A. Milman 1993 Social Impacts of Tourism: Host Perceptions. Annals of Tourism Research 20:650–665. Korean Casino Association 2002 Casino Visitors and Receipts. Seoul: Korea Casino Association. Labalme, J. 1994 The Great Riverboat Gamble: Southern Exposure. A Journal of Politics and Culture 22(1):10–14. Lee, C-K., and K-S. Kwon 1997 The Economic Impact of the Casino Industry in South Korea. Journal of Travel Research 36(1):52–58. Liu, J., and T. Var 1986 Resident Attitudes toward Tourism Impacts in Hawaii. Annals of Tourism Research 13:193–214. Lindberg, K., and R. Johnson 1997 Modeling Resident Attitudes toward Tourism. Annals of Tourism Research 24:402–424. Long, P. 1996 Early Impacts of Limited Stakes Casino Gambling on Rural Community Life. Tourism Management 17:341–353. Mason, P., and J. Cheyne 2000 Residents’ Attitudes to Proposed Tourism Development. Annals of Tourism Research 27:391–411. McCool, S., and S. Martin 1994 Community Attachment and Attitudes towards Tourism. Journal of Travel Research 32(3):3–11. Nunnally, J., and I. Bernstein 1994 Psychometric Theory. (3rd ed.) New York: McGraw-Hill. Perdue, R., T. Long, and L. Allen 1990 Resident Support for Tourism Development. Annals of Tourism Research 17:586–599. Perdue, R., T. Long, and Y. Kang 1995 Resident Support for Gambling as a Development Strategy. Journal of Travel Research 34(2):3–11. 1999 Boomtown Tourism and Resident Quality of Life: The Marketing of Gaming to Host Community Residents. Journal of Business Research 44:165–177. Pizam, A. 1978 Tourism’s Impacts: The Social Costs to the Destination Community as Perceived by its Residents. Journal of Travel Research 16(4):8–12. Pizam, A., and J. Pokela 1985 The Perceived Impacts of Casino Gambling on a Community. Annals of Tourism Research 12:147–165. Roehl, W. 1999 Quality of Life Issues in a Casino Destination. Journal of Business Research 44:223–229. Sekaran, U. 1992 Research Methods for Business. (2nd ed) New York: Wiley. Soh, J. 2000 The Korea Times (November 28):3. Submitted 8 October 2002. Resubmitted 29 March 2003. Accepted 14 April 2003. Refereed anonymously. Coordinating Editor: William R. Eadington
Annals of Tourism Research, Vol. 30, No. 4, pp. 868–885, 2003 2003 Elsevier Ltd. All rights reserved Printed in Great Britain 0160-7383/$30.00
doi:10.1016/S0160-7383(03)00060-4
PRE- AND POST-CASINO IMPACT OF RESIDENTS’ PERCEPTION Choong-Ki Lee Kyunghee University, South Korea Ki-Joon Back Kansas State University, USA Abstract: While most of the previous research on residents’ perceptions was conducted in the form of snapshots taken at a particular time, or after tourism development, this paper aims to explore any significant differences in these perceptions between pre- and post-development of casinos. The survey for this study was developed to examine the underlying relationships among impact, benefit, and support variables based on social exchange theory, using a structural equation model. The results show that positive economic impact was most significant in determining the benefit level, which was further enhanced after the casino opened. Respondents perceived positive social impacts to be most significant both before and after casino development. Keywords: casino, residents’ perception, structural equation modeling. 2003 Elsevier Ltd. All rights reserved. Re´sume´: Perceptions des habitants avant et apre`s l’ouverture d’un casino. En contraste avec la plupart des recherches ante´rieures sur les perceptions des habitants, qui repre´sentent des moments isole´s ou une pe´riode apre`s le de´veloppement du tourisme, le but du pre´sent article est de de´terminer s’il y a des diffe´rences significatives entre les perceptions avant et apre`s le de´veloppement d’un casino. On a formule´ une enqueˆte pour examiner les relations sous-jacentes entre l’impact, les be´ne´fices et les facteurs contribuants en se basant sur la the´orie de l’e´change sociale et en utilisant un mode`le d’e´quation structurelle. Les re´sultats montrent qu’un impact e´conomique positif e´tait l’e´le´ment le plus significatif pour de´terminer le niveau de be´ne´fices, qui e´tait ame´liore´ apre`s l’ouverture du casino. Les sonde´s trouvaient, avant et apre`s le de´veloppement du casino, que les impacts sociaux positifs e´taient les plus significatifs. Mots-cle´s: casino, perception des habitants, modelage d’e´quation structurelle. 2003 Elsevier Ltd. All rights reserved.
INTRODUCTION Several recent studies on the impacts of gaming legalization have been reported in the literature (Hsu 2000; Long 1996; Perdue, Long and Kang 1995; Roehl 1999). The impacts of legalizing Native-American gaming (Connor 1993), riverboat casinos (Labalme 1994), and land-based casinos (Pizam and Pokela 1985) are well documented, with their foundation stemming mainly from the impact studies of the 70s (Hsu 2000). Most research has focused on residents’ perceptions of
Choong-Ki Lee is Associate Professor in the College of Hotel & Tourism at Kyunghee University (Dongdaemun-ku, Seoul Korea. Email
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the social, economic, and environmental impacts of gaming on their communities. For instance, researchers have considered the impacts of gaming on crime rate, alcoholism, drug abuse, divorce rate, employment opportunities, tourist spending, tax revenues, disposable income, noise level, crowding, and traffic congestion. According to Carmichael, Peppard and Boudrea (1996), residents’ attitudes are important because they are rarely expressed in the political and development decisionmaking process. Their study results indicated that there was a growing awareness by local residents of both the negative impacts of rapid development and the positive employment benefits of casino development. Roehl (1999) compared Nevada residents’ attitudes toward the impacts of gaming in 1975 and 1992. The research found that the majority of respondents agreed that legalized casinos created more jobs, generated more money for the government, and allowed people to gamble more than they could afford to. However, the 1992 sample indicated that they were less likely to agree with the items “more money to run the government.” Long (1996) examined residents’ attitudes toward casino development in South Dakota and Colorado. Residents generally perceived a positive impact on employment, but a negative one in terms of traffic congestion, crowding, and crimes caused by gambling. The majority of respondents did not recommend legalized gambling to other communities. Attention to gaming development has also evolved into the exploration of the host community’s quality of life issues (Perdue et al 1995). Giacopassi, Nichols and Stitt (1999) worked with policy makers in seven communities that were new riverboat casino jurisdictions. Results showed that the majority of respondents favored the casino in the community and believed that it enhanced the quality of life by providing positive impacts on the economy. The perceptions or behaviors of residents can be explained by applying the social exchange theory, which attempts to understand and predict the behavior of individuals in an interactive situation (Ap 1990). Based on the literature, residents who perceive personal benefit from casino development will support and express positive attitudes toward it. Perdue, Long and Kang (1999) supported the social exchange theory in that such residents were more likely to be positive in assessing the quality of life. They also found that personal gains were strongly correlated with support for gambling and its positive impacts, such as jobs and recreation opportunities. The results revealed that the sociodemographic characteristics of respondents were not related to the perceived impact of gambling when controlling for personal benefits. Further, the support was a function of personal benefits, future of the community, positive and negative impacts of gambling, and quality of contact with gamblers. Korean Gaming Impact Although 13 casinos have been in operation at international hotels in Korea since the late 60s and early 70s, only foreigners are allowed
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to enter those establishments. Walker-Hill Hotel and Casino, the largest casino in Korea, is operating in the metropolitan Seoul, while eight are located in Cheju Island, experiencing oversupply due to diminished market demand. In 2001, some 626,000 tourists visited Korean casinos and their receipts totaled approximately $296 million (Korean Casino Association 2002). The survey results show that the Japanese represented 70%, followed by Mainland Chinese, the Taiwanese and Hong Kong Chinese (23%), and Americans (5%) (Lee and Kwon 1997). However, the government prohibited Korean people from entering the casinos because it worried about predominance of social issues, such as problem gambling, financial crisis, usury, and bankruptcy in the Confucian society. This might contribute to the fact that gaming impact studies in Korea are rare. But in December 1995, the Korean government legalized gaming in the run-down former coal mining areas (Chongsun, Taeback, Samcheok and Youngwol) of Kangwon province for domestic customers. These towns had once experienced a “gold rush” at a time in history when coal was used as a major source of energy for industries and households. As coal was replaced by new energy sources such as oil and gas, the economy of the communities rapidly declined. The Korean government pursued various economic revitalization policies, to no avail. After the four communities repeatedly demonstrated for legalizing gaming for domestic customers in order to revitalize the dilapidated mining towns, the government first legalized a casino in one of the four communities (Chongsun), and the Kangwon Land Casino opened for domestic customers in October 2000. This property has 30 table games and 480 slot machines and is attached to a deluxe hotel with 199 rooms. Prior to opening the property, the government expected to experience numerous positive economic impacts, including increased employment rate, income, sales revenue in local businesses, and so on. In 2002, 918,698 people visited the Kangwon Land Casino (2,517 players per day as compared to a planned capacity of 700 people) and generated $397 million (daily revenue of $1.1 million) (Kangwon Land Casino 2003). The casino hired 862 people from the communities, representing 37.1% of total employees (2,323) and paid $156 million for national and local taxes, including funds for communities in 2002 (Kangwon Land Casino 2003). Sales volumes have been increased in lodging facilities and gas stations by approximately 50%, restaurants by 20 to 30%, and railroad passengers by 300% (Kangwon Province 2000). Despite the many positive impacts of the casino, it began to cause serious problems, such as gambling addiction, usury, and bankruptcy. Local residents were reported to have lost tens of millions of won (US$1ⱌ1,200 won) within a few days of its opening (Soh 2000). A considerable number of patrons with “gambling mania” were obsessed with the possibility of hitting the jackpot or table games and tended to remain several days at the casino. “I came here five times and lost a total of 50 million won. I think more than 300 people are staying on a long-term basis”, said a man who stayed about 4 to 5 days whenever he visited the property (Soh 2000:3). A survey result indicated
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that typical players appeared to lose an average of 4.5 million won per visit (Choe 2000). The number of pawnshops rapidly increased just after the casino’s opening, and financial crises and problem gamblers were frequently reported by mass media. However, much of the qualitative data, including residents’ attitude toward the casino and social perspectives, have not been well reported. MEASUREMENT PROCESS AND IMPACT ANALYSES Homogeneous findings about residents’ perceptions of the different types of casinos, their location, and their history are well described. However, Mason and Cheyne stressed that “there are few studies on the perceived impacts of tourism either prior to any development or when it is not seen to be a significant economic area of activity for a region…studies have been in the form of snapshots taken at a particular time in a particular location” (2000:392). Therefore, this study aims to explore the changing attitudes of residents towards the preand post-development of casinos, using a structural equation model. Specifically, this study was conducted to explore the underlying factors affecting residents’ perceptions of casino development in terms of social, economic, and environmental impacts by constructing measurement scales. The study was also designed to investigate the significant differences in residents’ perceptions between pre- and post-development of casinos. Further, it was meant to examine the underlying relationships among impact, benefit, and support variables based on the social exchange theory, using a structural equation model. Characteristics of Respondents Two major casino communities—designated by a special law as rundown mining areas—were chosen for survey research. One consisted of two towns, Kohan and Sabuk, with a total population of approximately 16,000 in Chongsun County where the casino is located. This community is considered to be a direct impact area of it. Another community, Taeback City, is larger, with a population of approximately 60,000. It has a relatively well-developed operations, including lodging, restaurants, and other entertainment facilities, but is a 30-minute drive from the casino. Thus, this community is considered to be an indirect impact area from casino development. The data for this study were collected in two different time frames, through pre- and post-surveys. The former sample was taken at the end of June, 2000, before the casino opened. The number of samples was proportionately allocated based on occupation, using the official statistics of Chongsun County and Taeback City, Kangwon Province. A self-administered questionnaire was given to those who preferred to complete it by themselves, otherwise completed by the field researchers via personal interview. Respondents of at least 18 years of age were asked to participate in the survey and one person was chosen for the sample in cases of there being a group of people. To increase the response rate, small gifts were offered to those completing the ques-
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tionnaire. Respondents’ names and phone numbers were secured so that the same individuals could be used in the post-survey. A total of 517 usable questionnaires were ultimately collected during the prestudy. In December 2000, the post-survey was administered to those who had responded to the initial one after the Kangwon Land Casino opened. Each researcher was posted to the same site as in the presurvey and was given information on the respondent’s name, phone number, and work place as collected earlier. First, respondents were asked whether they had participated in the pre-survey. If they had, then the post-survey was conducted. Not all subjects were available, because they were out of town, moved away, quit their jobs, or closed businesses. On this occasion, a total of 404 usable questionnaires were finally collected, fewer than the initial sampling. Table 1 presents demographic characteristics of the respondents. The proportion of male participants (53.7%) was slightly higher than that of the female (46.3%). The majority were married (72.8%), aged 30 to 49 (63.6%), high school graduates (51.2%), and long-time residents of the city (21 to 40 years: 49.1%). The majority earned less than 2 million won (approximately $1,667) each month. Respondents who owned their houses totaled 50.2%, but those who owned land constituted only 15.8%. Of the respondents, 52.2% stated that they were born in the casino community. Measurement of Constructs A preliminary list of measurement items was initially generated from a review of the literature pertaining to residents’ perceptions toward tourism and casino impacts (Pizam 1978; Pizam and Pokela 1985; Liu and Var 1986; King, Pizam and Milman 1993; McCool and Martin 1994; Perdue et al 1990, 1995, 1999; Carmichael et al 1996; Long 1996; Jurowski, Uysal and Williams 1997; Lindberg and Johnson 1997). These items were then screened by scholars and community leaders of the casino town. They were asked to clarify these items, and comment whether the items were likely to be appropriate for evaluating residents’ attitudes toward the casino. After their comments, a pretest composed of the items was conducted with graduate students at one of the Korean Universities and with gaming community residents to further refine the list of items. Validity of dimensionality and intercorrelation was examined by factor analysis. Although it costs more than a mail survey, a direct face-to-face survey method was employed because of the expected improved response rate. Mail surveys have been commonly employed in the United States, but this technique appears to be inefficient in Korea due to a lower response rate. The theoretical model was developed based on the social exchange theory. As mentioned earlier, many researchers have utilized this conceptual frame to integrate factors influencing residents’ reactions to casino development. The social exchange theory assumes that their perceptions are affected by how they perceive the exchange they believe they are making (Gursoy, Jurowski and Uysal 2002). Jurowski
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Table 1. Demographic Characteristics of Respondents Characteristics Gender Male Female Age Less than 20 20–29 30–49 50 and higher Education Less than middle school High school 2-year college university or higher Monthly Income (1,200 won = US$1) less that 1 million won 1.00–1.99 million won 2.00–3.99 million won 4 million won or higher Length of Residence Less than 1 year 1–10 years 11–20 years 21–40 years 41 years or longer Marital Status Single Married Children in Household? Yes No Home Ownership Own Rent Land Ownership Own No Birth Place Here? Yes No
Percentage (N = 404)
53.7 46.3 3.0 25.7 63.6 7.7 11.9 51.2 16.1 20.8
40.6 41.3 16.8 1.3 1.2 20.1 24.8 49.1 4.7 27.2 72.8 48.5 51.5 50.2 49.8 15.8 84.2 52.2 47.8
et al (1997) state that residents’ support for tourism development should be considered as their willingness to take an exchange based on the social exchange theory. The model postulates that exogenous variables have both direct and indirect effects on benefit and support. The theoretical model tested, as shown in Figure 1, involved eight constructs: negative social, negative environmental, negative economic, positive social, positive
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Figure 1. Theoretical Model (1A) of Residents’ Perception toward a Casino
environmental, and positive economic factors, as well as benefit, and support. The negative social factor was composed of nine items: occurrences of gambling addicts, speculative gambling spirit, bankruptcy, destruction of family, prostitution, divorce, alcoholism, crime, and political corruption. The negative environmental factor was composed of six items: crowding due to tourists, traffic congestion, quantity of litter, noise level, water pollution, and destruction of natural environment. The negative economic factor comprised three items: cost of living, increased tax burden, and leakage of casino revenues. Further, positive social factor was composed of four items: quality of life, consolidation of community spirit, improvement of educational environment, and pride of local residents. The positive environmental factor was made up of two items: preservation of historic sites and natural beauty. The positive economic factor was composed of six items: investment and business, employment opportunity, tourist spending, tax revenue, public utilities and infrastructure, and standard of living. The benefit factor was made up of two items: How casino development provides benefits to the respondent and to the local community. The support factor was composed of five items: “I believe the future of our city is bright due to the casino industry”; “I am proud that I live in this city”; “the casino industry makes this city a better place to live”; “I support development of the casino”; and “the development of casino is the right choice for this city”. Residents’ perceptions were measured on a 5-point Likert-type scale (1 = strongly disagree, 3 = neutral, and
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5 = strongly agree). Respondents were asked to rate how much they agreed with each item on the scale. Reliability, Validity Tests and Confirmatory Factor Analysis A reliability test was used to assess the consistency in measuring the result. As Sekaran (1992) suggested, the coefficient alpha is the most popular measure of reliability for a multi-item scale. It was used to assess the internal homogeneity existing among the items in this study. The coefficient alpha estimates for the multi-item scales used in this study are presented in Table 2. All alpha coefficients for the data exceed or are close to the minimum standard for reliability of 0.7 recommended by Nunnally and Bernstein (1994) for basic research. Thus, the results indicate that these multiple measures are highly reliable for measuring each construct. Content validity ensures that the measure includes an adequate and representative set of items that would describe the concept. The lists of attributes used to measure negative/positive social, environmental, and economic factors, as well as benefits and supports, were selected after an extensive literature review, after interviews with tourism academics in the field of tourism impacts, and after interviews with community leaders of the casino town. All items were pilot-tested and respondents were asked to evaluate the appropriateness of the measuring instruments. It was evident that these research procedures ensured high content validity of the measurement instrument. Using LISREL 8.5, a maximum likelihood confirmatory factor analysis was undertaken to compare an 8-factor model with a different number of factor model and to assess the overall fit of the model. The 8factor model is composed of negative social, negative environmental, negative economic, positive social, positive environmental, and positive economic factors, as well as benefit and support. As shown in Table 2, the results indicated a good fit for the 8-factor model, χ2(597) = 1684.83; p = 0.00, χ2 / df = 2.82, RMSEA = .06, CFI = .91, NNFI = .90, for testing the model. Factor loadings for both pre- and post-survey data are listed in Table 2. Paired t-test between Pre- and Post-Perceptions of Residents Results of paired t-tests indicated that residents’ perceptions were significantly different before and after casino development, as shown in Table 3. Mean values of most variables were found to be lower in the post- than in the pre-survey. The results indicate that residents perceived all negative and positive impact factors as less strong after the casino opened than before. However, residents held stronger perceptions of some types of negative social impacts, such as gambling addiction, encouraging speculative gambling spirits, increasing bankruptcy rates, and destructive effects on the family, after casino development. Other types of negative impact factors scored significantly lower in the post- than in the pre-survey. For negative environmental impact
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Table 2. Results of Confirmatory Factor Analysis: Pre-Data and Post-Data Construct
Internal Consistencya
Negative Social 0.96
Item
Factor Loadings
Pre
Post
Gambling addicts Speculative gambling spirit Bankruptcy Destruction of family Prostitution Divorce Alcoholism Crime Political corruption
0.85 0.81 0.87 0.87 0.87 0.83 0.91 0.93 0.87
0.78 0.82 0.81 0.82 0.85 0.86 0.81 0.85 0.80
Negative Environmental
0.92
Crowding Traffic congestion Quantity of litter Noise level Water pollution Destruction of natural environ.
0.57 0.77 0.91 0.92 0.84 0.82
0.52 0.70 0.84 0.85 0.84 0.75
Negative Economic
0.68
Costs of living Tax burden Leakage of casino revenue
0.80 0.72 0.38
0.78 0.74 0.34
Positive Social
0.87
Quality of life Community spirit Educational environment Pride of local residents
0.53 0.64 0.80 0.50
0.65 0.73 0.85 0.69
Positive Environmental
0.68
Preservation of historic sites 0.73 Preservation of natural beauty 0.56
0.74 0.50
Positive Economic
0.72
Investment and business Employment opportunity Tourist spending Tax revenue Public utilities/infrastructure Standard of living
0.73 0.68 0.66 0.61 0.67 0.48
0.70 0.65 0.67 0.70 0.70 0.43
Benefits
0.75
Personal benefit Community benefit
0.67 0.88
0.71 0.85
Supports
0.82
Bright future Pride Better place to live Support Right choice for the city
0.81 0.77 0.43 0.82 0.72
0.83 0.80 0.72 0.85 0.81
Fit indices: χ 2(597) = 1684.83; p = 0.00, χ 2 / df = 2.82, RMSEA = .06, CFI = .91, NNFI = .90.
a
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factors, residents perceived that environmental concerns such as crowdedness, traffic problems, and noise levels were not really as bad as they had been thought earlier. Their scores were also lower regarding negative economic impact factors, indicating that they did not experience those economic problems as much as they had expected. On the other hand, residents perceived fewer positive impacts in the post-survey than were exhibited before. First, they scored somewhat higher than neutral about quality of life (mean = 3.11) and pride of local residents (mean = 3.19) during the pre-survey. During the followup sampling, they revealed more disagreement (mean = 2.34 and 2.55, respectively). Second, respondents also scored lower for positive environmental factors with regard to casino development. They perceived that it did not help preserve historic sites and natural beauty of the community. Third, they favored casino development when expressing concern about positive economic impact factors during the pre-survey (mean range from 3.42 to 4.00). Afterwards, their scores were much lower on positive economic impact factors. Standard of living scored especially low (mean = 2.32). Benefit factors scored slightly lower, indicating greater disagreement during the post-survey. Respondents also scored support factors slightly lower during the post-survey than before. They had neutral perceptions of benefit and support factors before the casino opening. These perceptions changed to be more negative as they experienced casino development. LISREL Methodology The hypothesized relationships between the factors shown in Figure 1 and the factor models described earlier were examined simultaneously using LISREL 8.5 (Joreskog, Sorbomm, du Toit and du Toit 2001). By using this program, the goodness of fit of the various models was testable and the relative fit of particular pairs of models could be assessed. Three approaches to ascertain consistency of factors were available using this methodology. The first answers the question, “Are there significant relationships among the variables? ” The second answers the question, “Is there a significant difference between the pre- and post- residents’ perceptions toward the Kangwon Land Casino development?” The third approach answers the question, “If there is a significant difference between two groups, what kind of differences occurred in supporting the casino development after the casino was opened?” These approaches were used: the first in models 1A and 1B, the second and third in models 2 and 3, described below. Model 1A, shown in Figure 1, specified the eight factors already described. In this model, direct and indirect paths from negative social, negative environmental, negative economic, positive social, positive environmental, positive economic factors, and benefit to support were specified, as well as direct paths from all those exogenous variables to benefit. To identify the best model for testing, a contrasting one (Model 1B) was created. This was the same as Model 1A with the exception that the direct paths from each exogenous variable to support
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Table 3. Results of Paired T-test between Pre- and Post-Survey Mean Scoresa
Variables
Negative social Occurrence of gambling addicts Speculative gambling spirits Bankruptcy Destruction of family Prostitution Divorce Alcoholism Crime Political corruption Negative Environmental Crowding due to visitors Traffic congestion Quantity of litter Noise level Water pollution Destruction of natural environment Negative Economic Cost of living Increased tax burden Leakage of casino revenues Positive Social Quality of life Consolidation of community spirit Improvement of educational environment Pride of local residents Positive Environmental: Preservation of historic sites Natural beauty Positive Economic Investment and business Employment opportunities Tourist spending Tax revenues Public utilities and infrastructure Standard of living Benefit Benefit to myself Benefit to local residents Support Future is bright due to casino I am proud that I live in this city Casino makes this city a better place to live I support casino development Casino is the right choice for this city
Mean Differenceb
|t-value|
Pre-survey
Post-survey
3.55 3.54 3.51 3.47 3.62 3.45 3.51 3.59 3.47
3.94 3.86 3.79 3.52 3.21 3.09 3.11 3.36 3.27
0.39 0.32 0.28 0.04 ⫺0.41 ⫺0.36 ⫺0.40 ⫺0.23 ⫺0.20
3.89 4.02 4.16 4.02 4.01 4.06
3.13 3.56 3.51 3.36 3.22 3.39
⫺0.76 ⫺0.46 ⫺0.64 ⫺0.66 ⫺0.79 ⫺0.67
11.63∗ 6.76∗ 9.05∗ 9.39∗ 10.88∗ 8.91∗
3.24 3.35 3.78
2.93 3.09 3.54
⫺0.32 ⫺0.26 ⫺0.24
4.67∗ 3.97∗ 3.08∗
3.11 2.76 2.38 3.19
2.34 2.50 1.99 2.55
⫺0.77 ⫺0.26 ⫺0.39 ⫺0.64
11.94∗ 3.63∗ 6.02∗ 9.34∗
2.88 3.72
2.47 3.09
⫺0.41 ⫺0.63
7.10∗ 8.94∗
3.96 3.63 3.94 3.73 4.00 3.42
2.89 3.01 3.17 3.01 3.21 2.32
⫺1.07 ⫺0.62 ⫺0.78 ⫺0.72 ⫺0.79 ⫺1.10
15.73∗ 9.58∗ 11.96∗ 11.29∗ 11.95∗ 19.26∗
3.18 3.27
2.43 2.91
⫺0.75 ⫺0.36
10.41∗ 4.87∗
3.48 2.85 3.15
3.15 2.63 2.66
⫺0.33 ⫺0.22 ⫺0.49
4.42∗ 3.23∗ 6.51∗
3.22 3.33
3.02 3.00
⫺0.20 ⫺0.33
1.93 4.26∗
4.57∗ 3.74∗ 3.41∗ 0.52 5.22∗ 4.79∗ 4.81∗ 2.79∗∗ 2.55∗∗
∗ pⱕ.001; ∗∗ pⱕ.01. a Based on a mean value on a 5 Likert-type scale, where 1=strongly disagree, 3=neutral, and 5=strongly agree. b Mean difference [post-pre survey].
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were eliminated. A well-fitting Model 1A or 1B provides a degree of support for the general theoretical construct used as a basis for the present test for the effects of exogenous variables (impact factors) on endogenous variables (benefit and support) and group analysis between pre- and post-data. The significance of the path coefficients would provide a test for various aspects of the general theoretical model. For assessing the goodness-of-fit, chi-square analysis, Brown and Cudeck’s (1993) root mean square of approximation error (RMSEA), Bentler’s (1990) comparative fit index (CFI), and Bentler and Bonett’s (1980) non-normed fit index (NNFI) were performed. Since the chisquare is almost always significant with large sample sizes, the primary evaluations of fit appropriate for this study are the RMSEA, CFI, and NNFI. Models are generally considered to have good fits if RMSEA is lower than 0.08 and CFI and NNFI are greater than 0.90, although models with indices lower than this may be acceptable in some circumstances (Bentler and Bonett 1980; Browne and Cudek 1993). The two models (1A and 1B) were compared by the chi-square difference test (the differences in its values with degrees of freedom equal to the difference in degree of freedom) and by the difference in the RMSEA, CFI, and NNFI. If the chi-square difference is not significant, the models are judged to be equivalent. The CFI and NNFI are measures of the proportionate improvement fit as one moves from the baseline to the target model. For testing similarity between pre- and post-data, the two groups are not restricted by any of the non-fixed parameters so that they have the same value across groups. This least demanding test of similarity is the same as that in basic model 1A or 1B. If the fit is satisfactory, one moves to the next step by constraining the factor loadings to be equal across the two groups. This was referred to as Model 2. By comparing the goodness of fit between this and the previous one, it could be decided whether the two groups have the same loadings or coefficients from the measurement model. Assuming that constraining the factor loadings to be equal is justified based on the previous two models, the next hierarchy is to constrain all regression weights to be equal across groups. This restricted hierarchical model, Model 3, helps to assess invariance for the general structural equation model with latent variables (Bollen 1989). Once a hierarchy is established, it is possible to assess which degree of invariance best matches the data. Finally, one can measure the invariant effects of exogenous on endogenous variables in the two groups. Model Results Table 4 summarizes the goodness-of-fit results for models 1A, 1B, 2, and 3. The overall fit of Model 1A was good. The chi-square was significant (p < .001), while the CFI and NNFI were about .91 and .90, respectively; whereas RMSEA was .062, implying that the model was appropriate to test further. The overall fit of Model 1B was inferior to that of Model 1A. By constraining the direct effects of exogenous vari-
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Table 4. Summary of Goodness-of-fit Indices Model Model Model Model Model a
1A 1B 2 3
Chi-squarea
df
RMSEA
CFI
NNFI
3758.21 3918.99 3808.20 3970.68
1194 1206 1223 1236
0.062 0.067 0.063 0.067
0.91 0.89 0.91 0.88
0.90 0.88 0.90 0.87
p < .001.
ables (impact factors) on endogenous variable (support) in Model 1B, the statistical indicator of fit (chi-square) and practical indicators of fit (CFI and NNFI) were significantly different from Model 1A (⌬χ 2 = 160.78, ⌬df = 12, p < .001). Thus, it was decided to use Model 1A as a base model for further testing the invariance between pre- and post-data. The overall fit of Model 2 was good. Both the chi-square and practical indicators of fit were significant. For assessing the difference between Model 1A and Model 2, the chi-square and practical indicators were compared, resulting in no significant difference (⌬χ 2 = 49.99, ⌬df = 29). Since Model 2 had equality constraints in factor loadings for pre- and post-data, the result showed that two groups have an invariant structure of manifest variables for indicating latent variables. In other words, the data have similar underlying structures in each factor. As Bollen (1989) suggests, the next step is to assess the invariance of all regression coefficients across groups by adding equality constraints in Model 3. This higher step in the hierarchy assessed the invariance for the general structural equation model with both latent and manifest variables. As Table 4 presents, the overall fit of Model 3 appeared to be good. However, by restricting Model 3 with both factor loadings and path coefficients, the chi-square difference test and practical indicator difference test showed that the fit of Model 3 was significantly worse than that of Model 2 (⌬χ 2 = 162.48, ⌬df = 13, p < .001). Thus, the hierarchical structural equation modeling step stopped in Model 2, suggesting that pre- and post-data had similar underlying structures, but were significantly different with regard to the relationship among latent variables. Figure 2 presents the maximum-likelihood parameter estimates for both pre- and post-data. In the former, support was directly significantly predicted by both negative and positive social, positive environmental, positive economic factors, and benefit (P’s < .01). Support was also indirectly predicted by negative and positive economic factors through benefit (P’s < .01). Among those predictors, all positive factors had significantly direct effects on support, whereas the negative environmental factor had no significant effect on either benefit or support. In the post-data, Figure 2, support was directly significantly predicted
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Figure 2. Standardized Parameter Estimates (Model 2): Pre- and Post Data
by negative and positive social factors, as well as by benefit (P’s < .01). It is interesting to note that the high degree of invariance of the effects of negative and positive social factors and benefit on support was observed in between the data. Direct effects of positive environmental and positive economic factors on support were not found significant in the post-data. For assessing the indirect effect of predictors on support, negative and positive economic impacts were significantly mediated by benefit. Specifically, the negative economic factor was slightly decreased in its effect on benefit (β = ⫺.08, t = 2.53) as compared to pre-data. However, the positive economic factor was significantly increased in its effect on benefit (β = .47, t = 6.12).
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Study Findings The results of the analyses reported here suggest that in many respects, residents showed significantly different perceptions about the casino before and after its development. The underlying structure of each factor was very similar between the pre- and post-data, which suggested that the measurement items had high reliability. Prior to casino development, residents’ support was predicted by negative social factors and positive social, environmental, and economic factors, as well as by benefit. As Figure 2 shows, respondents supported casino development when they perceived receiving personal and community benefits (β = 0.25; t = 5.06) as supported by social exchange theory. They also offered support when concerned about positive social, environmental, and economic factors, such as quality of life, preservation of historic sites and natural beauty, employment opportunity, and standard of living. However, when concerned about negative social factors, such as occurrences of gambling addiction, prostitution, divorce, alcoholism, crime, and political corruption, their support level decreased dramatically (β = ⫺.16, t = 3.01). Negative environmental factors did not predict their support level. It is interesting to note that both negative and positive economic factors had significant effects on their perception about benefit. Although this factor was simply measured by asking for their agreement or disagreement about statements—“the development of casino provides benefits to myself”, “the casino development provides benefits to local residents”—they interpreted these statements as relating more to financial benefits. Thus, both positive and negative economic impacts on benefit were most significant among exogenous variables. Furthermore, positive economic factors had the strongest effect on residents’ support level (β = 0.33, t = 5.87). After the casino development, post-data showed some changes in residents’ support level. Figure 2 exhibits the significant effect of perceptions about benefit on the attitude toward support for casino development. Benefits showed the strongest effect on support (β = 0.66, t = 8.87), whereas positive environmental and economic factors had no significant direct effects on support. Furthermore, positive social factors had less effect on support for post data (β = 0.26, t = 2.76) as compared to pre-data (β = 0.29, t = 4.46). It is interesting to note that positive economic factors had significant effects on support only when mediated by benefit. This result can be interpreted as residents’ exposure to the casino sector that their perceptions of positive environmental and economic factors did not cause them to support it. Rather, they tend to become more supportive when they actually received benefit, specifically economic benefit. The results also showed that those residents’ perceptions about negative social factors were less likely to be predicted by their level of support for the casino after its opening.
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CONCLUSION Given the paucity of research reported in the tourism literature on the related issues, this study sheds light on identifying differences in residents’ perceptions between pre- and post-casino development. The primary intention of this research aimed at exploring residents’ subjective perceptions of changes between pre- and post-development of casinos by measuring levels of economic, social, and environmental impact factors. This study also sought to ascertain how each impact factor determined residents’ benefit and support levels based on the social exchange theory. Confirmatory factor analysis showed six underlying dimensions measuring residents’ perceived impacts of casino development. Results of paired t-tests showed that most of these impact factors scored lower in post-data. Respondents perceived fewer negative impact factors and were less favorable about positive impact factors. Respondents scored low for both benefit and support, indicating that they were not receiving much benefit from casino development and their support level was lower after the casino opened. Results indicated that respondents’ support level was directly influenced by benefit. Although the mean values of benefit and support decreased significantly upon the casino’s opening, the causal relationship became more significant between those two variables. Both positive and negative economic factors showed significant impacts on support when residents actually received benefits. These findings confirmed the study by Perdue et al (1995) that results for residents showed a positive correlation between their support level for casino development and job opportunities and other types of economic impacts. The causal relationship between the positive social impact factor and support level remained the same, whereas negative social impact on support level slightly decreased. These significant economic and social impacts on benefit and support level confirm the social exchange theory. However, environmental impact did not significantly predict either the benefit or support level in post data. In summary, the results showed that positive economic impact was most significant in determining the benefit level, which was further enhanced after the casino opened. Respondents perceived positive social impacts to be most significant in affecting the support level both before and after casino development. The results of the structural equation modeling approach suggest several implications: the social exchange model fits very well in explaining residents’ attitudes toward casino operation in both pre- and post-survey data; policymakers should identify how to provide benefits to local residents so that they can support casino development further; and casino operators and policymakers should make efforts to minimize the negative social impacts, because increase in the level of quality of life or standard of living was not only due to the positive economic impact but also was significantly affected by negative social factors, such as gambling addiction problems. One limitation of the study was that the post-data were collected
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only six months after collection of the pre-survey data. Participants could still remember their responses, causing history bias. The postdata collected for shorter periods of time may also underestimate the impact of casino development if residents have not accessed the casino. In addition, newly-introduced casino development might initially inflate perceptions, due to a short-lived period of residents’ excitement over access to the casino (“novelty effect”), which was not the case in this specific case. Thus, a longitudinal study is strongly recommended to investigate residents’ perceptions of casino development over time, preferably examined on an annual basis. Currently, the authors are conducting a follow-up study for assessing the various impacts of casino development on residents’ quality of life, perception toward benefits, and support level. By conducting a series of longitudinal studies, the difference between resident perceptions and realities will have narrowed even more and researchers should be able to evaluate changes in residents’ attitudes and to identify new positive or negative impacts of the casino. Therefore, policymakers can take appropriate actions to make the community a pleasant place to A live and to improve the quality of life for residents.왎 REFERENCES Ap, J. 1990 Residents’ Perceptions Research on the Social Impacts of Tourism. Annals of Tourism Research 17:610–616. Bentler, P. 1990 Comparative Fit Indexes in Structural Models. Psychological Bulletin 107:238–246. Bentler, P., and D. Bonett 1980 Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychological Bulletin 88:588–606. Bollen, K. 1989 Structural Equations with Latent Variables. New York: Wiley. Browne, M., and R. Cudeck 1993 Alternative Ways of Assessing Model Fit. In Testing Structural Equation Models, K. Bollen and J. Long, eds., pp. 445–455. Newbury Park CA: Sage. Carmichael, B., D. Peppard, and F. Boudreau 1996 Mega-Resort on My Doorstep: Local Resident Attitudes toward Foxwood Casino and Casino Gambling on nearby Indian Reservation Land. Journal of Travel research 34(3):9–16. Choe, Y. 2000 The Korea Herald (November 22):11. Connor, M. 1993 Indian Gaming: Prosperity, Controversy. International Gaming and Wagering Business 14(1):1–45. Giacopassi, D., M. Nichols, and B. Stitt 1999 Assessing the Impacts of Casino Gambling on Crime in Mississippi. American Journal of Criminal Justice 18:117–131. Gursoy, D., C. Jurowski, and M. Uysal 2002 Resident Attitude: A Structural Modeling Approach. Annals of Tourism Research 29(1):79–105. Hsu, C. 2000 Residents’ Support for Legalized Gaming and Perceived Impacts of Riverboat Casinos: Changes in Five Years. Journal of Travel Research 38:390–395. Joreskog, K., D. Sorbom, S. du Toit, and M. du Toit 2001 LISREL 8: New Scientific Features. Chicago: Scientific Software International.
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Jurowski, C., M. Uysal, and D. Williams 1997 A Theoretical Analysis of Host Community Resident Reactions to Tourism. Journal of Travel Research 36(2):3–11. Kangwon Land Casino 2003 Report on Casino Visitors and Revenues. Kangwon Province: Kangwon Land Casino. Kangwon Province 2000 Evaluation Report on Initial Operation of Kangwon Land Casino. Kangwon Province: Internal government material. King, B., A. Pizam, and A. Milman 1993 Social Impacts of Tourism: Host Perceptions. Annals of Tourism Research 20:650–665. Korean Casino Association 2002 Casino Visitors and Receipts. Seoul: Korea Casino Association. Labalme, J. 1994 The Great Riverboat Gamble: Southern Exposure. A Journal of Politics and Culture 22(1):10–14. Lee, C-K., and K-S. Kwon 1997 The Economic Impact of the Casino Industry in South Korea. Journal of Travel Research 36(1):52–58. Liu, J., and T. Var 1986 Resident Attitudes toward Tourism Impacts in Hawaii. Annals of Tourism Research 13:193–214. Lindberg, K., and R. Johnson 1997 Modeling Resident Attitudes toward Tourism. Annals of Tourism Research 24:402–424. Long, P. 1996 Early Impacts of Limited Stakes Casino Gambling on Rural Community Life. Tourism Management 17:341–353. Mason, P., and J. Cheyne 2000 Residents’ Attitudes to Proposed Tourism Development. Annals of Tourism Research 27:391–411. McCool, S., and S. Martin 1994 Community Attachment and Attitudes towards Tourism. Journal of Travel Research 32(3):3–11. Nunnally, J., and I. Bernstein 1994 Psychometric Theory. (3rd ed.) New York: McGraw-Hill. Perdue, R., T. Long, and L. Allen 1990 Resident Support for Tourism Development. Annals of Tourism Research 17:586–599. Perdue, R., T. Long, and Y. Kang 1995 Resident Support for Gambling as a Development Strategy. Journal of Travel Research 34(2):3–11. 1999 Boomtown Tourism and Resident Quality of Life: The Marketing of Gaming to Host Community Residents. Journal of Business Research 44:165–177. Pizam, A. 1978 Tourism’s Impacts: The Social Costs to the Destination Community as Perceived by its Residents. Journal of Travel Research 16(4):8–12. Pizam, A., and J. Pokela 1985 The Perceived Impacts of Casino Gambling on a Community. Annals of Tourism Research 12:147–165. Roehl, W. 1999 Quality of Life Issues in a Casino Destination. Journal of Business Research 44:223–229. Sekaran, U. 1992 Research Methods for Business. (2nd ed) New York: Wiley. Soh, J. 2000 The Korea Times (November 28):3. Submitted 8 October 2002. Resubmitted 29 March 2003. Accepted 14 April 2003. Refereed anonymously. Coordinating Editor: William R. Eadington