Presenting a regional advantage evaluation model for industry development based on the anchor regional system

ABSTRACT
This research aims to present a model for evaluating the regional competitiveness capacity using an anchor-based system. The research method employed a descriptive-analytical approach, utilizing a questionnaire based on AHP and a cross-sectional structure analysis model (37x37) for data collection. The analytical framework is based on the three concepts of importance, effectiveness, and sensitivity of the variables in the three constituent components of the anchor model, including 1-attraction and establishment of the anchor tenant, 2-clustering and mobilization of multi-scale resources, and 3-intelligence and stabilization of the leadership position. Based on the obtained results, in terms of effectiveness, the variables of industry chain with the rate of 1.76 and the quality and quantity of infrastructure (1.14) had the most significant impact on the other investigated variables in terms of effectiveness. In the sector of clustering and mobilization of multi-scale resources, the best performance related to the variables of the presence of multinational and international companies in the region with (1.46) and the level of density of private and public companies along with the level of knowledge and technology transfer of global industry to the region in the current situation with a score of (1.16) has been. In the area of intelligence and stabilization of the leadership position in terms of effectiveness, the highest performance is for the variables of the existence of industry innovation support centers (0.84), the existence of regional and national innovation systems in the industry sector (0.78), and the degree of interdependence between existing companies and investors and regional stakeholders (0.78). Finally, based on the results of the discussion, 7 principles and 3 hypotheses were formulated and presented.
Extended Abstract
Introduction
To evaluate the competitive capabilities of regional industry development, particularly in new industries across different regions, various routes and models have been employed. The classic model of regionalism, or endogenous models, and the value chain of globalism, or exogenous models, are among those that have provided a framework for measuring the competitive advantages of developing new industries in the region. Also, most competitiveness models have introduced the physical aspects of resources, such as labor, market, water, mineral resources, infrastructure, and energy of the regions, as a competitive advantage, and have paid less attention to the social, economic, and geographical advantages. The purpose of this research is to present a new model and framework for regional industry development planners to evaluate the competitive advantage of regions.
 
Methodology
This research is descriptive and analytical in terms of method and exploratory in terms of purpose. The methods of collecting information are documentary and library. The data collection tool in this research was a questionnaire. In this research, first, the variables related to the anchor theory were counted and extracted. After extracting the variables, they have been defined and separated into three categories as attracting and establishing the anchor tenant, clustering and mobilization of multi-scale resources, and intelligence and stabilization of the leadership position. After counting the variables, items related to the evaluation literature were formulated for each. After compiling the items, the data related to them was collected. The opinions of experts in regional planning and development were used to analyze and examine the variables.
The number of the sample community was determined based on the available people. Based on available people, 17 regional, planning, and economic experts were selected as a sample community. The questionnaire was a tool for collecting information. Questionnaires were designed in two ways. The first questionnaire was used to measure the importance of variables in terms of attracting and establishing anchor tenants, clustering and mobilization of multi-scale resources and intelligence, and stabilization of leadership positions in the form of a matrix. The second questionnaire is designed to measure the influence of variables in a cross-sectional manner.
AHP hierarchical analysis, cross-structural analysis (using a 37*37 matrix), and SPSS statistical models have been employed in the data analysis. From the AHP model to determine the importance of variables in pairs concerning the three criteria of attraction and establishment of anchor tenants, clustering and mobilization of multi-scale resources and intelligence, and stabilization of the leadership position, and from the cross-structural analysis model to measure the degree of effectiveness and sensitivity of the variables to each other, and the correlation model has been used to examine the relationships between the components. To discuss the results and present the model, three monitoring concepts of "effectiveness," "importance," and "sensitivity" were used to analyze the variables.
 
Results and discussion
Based on the results, in terms of effectiveness, the variables of industry chains (1.76) and the quality and quantity of infrastructure (1.14) had the most significant impact on the other investigated variables in terms of effectiveness. In this section, the variables with the lowest influence are labor attraction power (0.22) and the level of legitimacy of local managers in decision-making and decision-making by national managers (0.3).
In the clustering and mobilization of multi-scale resources, the best performance is related to the variables of the presence of multinational and international companies in the region, with a score of 1.46, and the density level of private and public companies, along with the level of knowledge and technology transfer of global industry to the region, with a score of 1.16. The lowest effective performance is also related to the indicators of the level of friendship and unreceptiveness of local people (0.3) and the level of concentration of financial institutions (0.38).
In the area of intelligence and stabilization of the leadership position in terms of effectiveness, the highest performance is for the variables of the existence of industry innovation support centers (0.84), the existence of regional and national innovation systems in the industry sector (0.78), and the degree of interdependence between existing companies and investors and regional stakeholders (0.78).
In the attraction of anchor tenants, the variables of industry chains (1.84) and the enthusiasm of local stakeholders in the establishment of industries (1.05) have the highest sensitivity compared to other variables. The lowest sensitivity is related to tax discount (0.11) and the quality of industry managers in terms of industry management (0.14).
In terms of the sensitivity of the variables to other variables in the clustering and mobilization of multi-scale resources, the highest sensitivity is related to the presence of multinational and international companies in the region (1.65) and the presence and level of loyalty of foreign investors to the region (1.81).
In the field of intelligence and stabilization of leadership positions, the variable sensitivity of strategic orientation of companies to multiple industries (1.16) has the highest sensitivity compared to other variables.
Based on the results obtained in the anchor tenant attraction section, the variables of industry chain with the amount (1.34) and the quality and quantity of infrastructure (2.73) had the highest importance compared to other investigated variables. In this section, the variables with the least importance are labor attraction power (0.27) and the local government's level of belief in the possibility of creating industries (0.29).
In the sector of clustering and mobilization of multi-scale resources in terms of effectiveness, the best performance is related to the variables of the existence of programs and policies for the formation of industry clusters with a score of 2.41, and the degree of financial connection of the region with the global financial market with a score of 2.22, and the lowest performance is in effectiveness. It is also related to the indicators of the level of connection of existing local industries with global industries (0.3) and the level of connection of the local workforce with modern global knowledge (0.52).
In terms of importance, in terms of intelligence and stabilization of the leadership position, the highest importance is for the variables of space and auxiliary infrastructure for the expansion and diversification of industry (2.33), the existence of the regional and national innovation system in the industry sector, and the motivation of research and development capabilities in existing industries (1.89).
 
Funding
There is no funding support.
 
Authors' Contribution
The authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
The authors declared no conflict of interest.
 
Acknowledgments
 We are grateful to all the scientific consultants of this paper.