Determination of Recreational Opportunity Spectrum Factors in potential tourism areas using Structural Equations Modeling (Case study: Golestan province)

Tourism Impact Assessment is importance due to population growth and having high sensitivity of natural recreational resources. In this study, attempting to introducing process of Recreational Opportunity Spectrum in the field of visitor impacts management for developing tourism in possible level of carrying capacity. The study area is part of Golestan province (three Towns: Gorgan, Kordkuy and Aliabad). Structural equation modeling was used to obtain the relationships between factors. The research findings showed that the Recreational Opportunity Spectrum was not influenced by the Moderating of demographic characteristics. Therefore, 97% of the changes in the Recreational Opportunity Spectrum Latent variables are explained by access, onsite management, social interaction, acceptability of visitor impacts, acceptable regimentation, and non-recreational resource uses. The relation of Latent variables, with the exception of, non-recreational resource uses with Recreational Opportunity Spectrum, is positive and significant at 1% level. The regression equation obtained in this study can be used in tourist planning by Managers of recreational area, especially in potential similar areas. It is expected that with the application of Structural Equation Models in the tourism sector, the negative effects of this activity on the nature will be minimized.

It should be noted that, despite the desirable impacts of low recreational utilization levels, it can also lead to negative impacts. Therefore a certain level of effects is acceptable (Santiago et al. , 2008, p. 905؛ mikaeili & Dazyani, 2012). One of the management frameworks to control the visitor's impacts or determine carrying capacity is Ecotourism Opportunity Spectrum (Clark & Stankey, 1979:2)

In this research, the effect of access, onsite management, social interaction, acceptability of visitor impacts, acceptable regimentation, and non-recreational resource uses latent variables on recreational opportunity spectrum has been investigated using Sstructural Equation Modeling (SEM). In this model, a set of indicators is used to measure a concept which is called latent variable (Garson, 2017, p. 47). Smart PLS 3. 2. 7 Software (Ringle et al. , 2015) which uses partial least squares (PLS) for fitting the model was used for analysis of structural equation models.

Discussion and The data of this research was evaluated and finalized in three stages: 1) measurement model; 2) structural model; 3) overall model. But as mentioned above, if there is a lack of heterogeneity between the data, the result of this run will be valid. In this paper, the lack of heterogeneity between the data was examined and this issue was not confirmed. So the traditional PLS results will be valid.
As shown in Fig. 1, according to view of the visitors in the study area, 97% of the changes in recreational opportunity spectrum latent variable is explained by its Composed elements which is confirmed in studies (Yamaki et al. , 2003, p. 57; Zulian et al. , 2013). Overall, in this study, in addition to the degree of importance of each composed factors, the degree of importance of each composed indicators of each factor was also determined.

Using the derived formula, we can extract the first and the end of the spectrum by placing the lowest and highest Likert spectrum (1 and 5), And then using this formula and determining its numerical value by placing the results of the questionnaire in the study area, can be determined the position of the spectrum of opportunity