Designing a Model for Effective Strategic Factors in Attracting Investors to the Tourism Industry Using a Mixed-Method Approach

As the goals outlined in the Iranian Strategic Tourism Development Policy Document cannot be realized without attracting financial resources, this study sought to design a model for effective strategic factors in attracting investors to the tourism industry in Ardabil province using the mixed-method approach. The population of the study at the qualitative phase comprised managers and officials of the Directorate General for Cultural Heritage, Handicrafts, and Tourism of the province, potential and real professionals and investors in tourism, and university experts with at least five years of experience in tourism management, investment, and marketing. The sample size of the study was selected through purposive sampling. To collect the required data, semi-structured interviews were conducted with the participants of the study until theoretical saturation was achieved in the twenty-fifth interview. The collected data were then analyzed via systematic grounded theory using open, axial, and selective coding. Accordingly, fifty-four concepts, eighteen sub-categories, and seven main categories were found after the integration and classification of concepts. Based on the proposed model of the study, the investors’ tendency to invest in tourism, tourism environment, and investment attraction management, the quality of tourism infrastructure, social factors prevailing over the region, supportive measures, and strengthening the position of tourism were identified as the core category, causal conditions, contextual conditions, intervening conditions, strategy, and the result of the implementation of strategies, respectively. In the quantitative phase, a questionnaire was developed by the researchers based on the identified components. Then, the questionnaire was administered to 220 investors and experts in the field of tourism. The collected data were then analyzed through the Smart PLS Software using structural equation modeling. Finally, the validity and correlation among the model’s variables were reconfirmed.