Introduction

The tourism industry has expanded at an increasing growth rate during the last decade and has been a significant source of income for many developing countries. But Iran, despite its high capabilities, has allocated a small share of this industry. One of the strategies used by countries to attract tourists is to create identifiers for tourist destinations. One of the important tools in identifying these destinations and any kind of business is using geomarketing. One of the important strategies for the success of any business is the implementation of geomarketing or location-based marketing. Location-based marketing is the main building block of successful businesses. Location-based marketing reduces risk and realizes it’s potential. On the other hand, using location-based marketing can improve business.

Methodology

The research method is descriptive-analytical and in terms of purpose, applied-developmental. The statistical population of the research consists of 31 specialists, 42 managers, 25 experts and 36 related professors in Lorestan province, totaling 134 people, the sample size is based on Cochran's formula and randomly stratified A total of 100 people were selected. Tools such as questionnaire, Lorestan tourism map and Arc GIS software have been used. Questionnaire designed in three parts with 39 questions including geomarketing questionnaire by Zargoza et al. (2019) with 15 items as independent variable, geotourist attraction questionnaire by Mamiz et al. (2012) with 7 items as dependent variable and destination brand image questionnaire by Konnik and Gartner (2007) with 17 items as mediating variables. The validity of the questionnaire was in a formal way, which was approved by experts and professors, and its reliability was confirmed by Cronbach's alpha of 0.84. Data analysis is based on structural equation analysis in Smart PLS software.

Results

Based on the average and standard deviation of research variables, geotourist attraction variable has the highest mean and destination brand image variable has the lowest mean, and geomarketing variable is after geotourist attraction variable. Also, the results of the standard deviation show that the geomarketing variable has the highest standard deviation, which means that the dispersion of responses in this variable is high, and the destination brand image variable has the lowest standard deviation, which shows that the dispersion of the responses is low compared to other variables. Variables.
Also factor loadings at the level of 0/001 they are meaningful. Based on the results, the significance level for the factor loadings or standard regression coefficients of the 3 observed variables has not been reported. This is because these variables are respectively considered as reference variables or representative variables for the 3 variables of destination brand image, geotourist attraction and geomarketing, so that these hidden variables are without scale and in other words without root and unit of measurement. They should be fixed. The AVE measure represents the average variance shared between each construct with its indicators. In simpler terms, AVE (Average Variance Extracted) is used for convergence validity and shows the high correlation of indicators of one structure compared to the correlation of indicators of other structures. The value of this coefficient varies from zero to one, and values ​​higher than 0.5 are accepted. Convergent validity or average variance extracted (AVE) was obtained for geomarketing 0.836, destination brand image 0.825, geotourist attraction 0.816, also the value of structural reliability coefficient or composite reliability (CR) is variable from zero to one, with values ​​higher than 0/7 is accepted, which was obtained for the index of geomarketing 0.785, destination brand image 0.765, geotourist attraction 0.756, which shows the appropriateness of these subscales. In fact, all variables and its components have appropriate construct validity.
In this research, it has been used to check the reliability of the model variables according to the opinion of Fornell and Locker (1981). Unlike Cronbach's alpha, which implicitly assumes that each index has the same weight, composite reliability relies on the true factor loadings of each construct; and thus provides a better measure of reliability. The composite reliability should reach a value of more than 0.07 to indicate the internal stability of the structure, which in this study, the composite reliability value of all variables is greater than 0.07. It should be noted that in the modeling of structural equations with the help of Pls method, unlike the covariance-based method, there is no index for the overall evaluation of the model. But an index called Goodness of Fit (GOF) was proposed by Tenenhaus et al. (2005). This index considers both measurement and structural models and is used as a measure to measure the overall performance of the model. In this research, the value of GOF index is 0.52, which indicates the overall desirability of the model.
 

Discussion

Based on the results, it can be said that geomarketing and the use of new geographic technologies have been effective in identifying tourist attractions such as Lorestan waterfalls and also in persuading geotourists to visit these attractions in Lorestan province. The mind and psyche of the tourist created a good memory of these attractions, based on this, it will create the ground for revisiting and creating a suitable brand image, and this will attract more and more tourists to Lorestan waterfalls. Therefore, the mediating role of the destination brand image has worked well in this context and will become the basis for the increase of tourists in Lorestan waterfalls as one of the main and most well-known geotourist attractions.
 

Conclusion

Based on the findings, it can be said that in general, geomarketing has had an impact on attracting geotourists with the mediating role of destination brand image in Lorestan waterfalls. In the meantime, geomarketing has influenced the attraction of geotourists with a path coefficient of 0.43, geomarketing on the image of the destination brand with a path coefficient of 0.72, and the image of the destination brand on the attraction of geotourists with a path coefficient of 0.30. Based on this, the effect of geomarketing on the destination brand image has the highest effect and the effect of the destination brand image on geotourist attraction has the lowest effect.