| چکیده انگلیسی مقاله |
IntroductionCities play a vital role in the lives of the vast majority of people, yet face great challenges beyond regions, nations and continents, and most cities are the main drivers of change. Recent technological developments have renewed belief in the positive impacts of ICT and other innovative technologies in the city. The combination of smart solutions (active technology) to address major social challenges and focus on the city as key drivers of change has led to the concept of smart city. Finally, scientific studies on smart city development readiness are largely limited to developed countries. And the literature on smart city readiness in developing countries is at an early stage and needs more empirical support. The transformation of a city into a smart city requires considerable efforts by political representatives, managers, residents, entrepreneurs, as well as its various communities. The concept of smart city is evolving rapidly, and the attention of the world as a promising response to the challenge of urban sustainability in large and small cities. Given the different structure of Iranian cities, this study seeks to localize the indicators and analyze their interactions for any planning and management. MethodologyThe study of the components of smart city in Zanjan deals with 6 components, 30 benchmarks and 100 standard indices designed in scientific societies around the world. By method, the present study is a descriptive field type. In order to carry out the research, the research components, criteria and indices were extracted from the theoretical foundations of the research and then prioritized by in-depth experts. Library and field methods have been used to collect the required information. Due to the lack of familiarity of most experts with the concepts of smart city, only experts in the statistical population who were familiar with the concept of smart city were included. The sampling method was theoretical saturation and by this method in 21 samples we achieved our goals. In this study, first, the importance and impact of indicators on the intelligence of cities were measured and analyzed by SPSS. Then, the extent of impact as well as the interactions between the components, criteria and indices were measured and analyzed with Micmac software. Because interviews and questionnaires have been used, the research is a composite (sequential exploratory model). And the composition is in the concluding phase. Both questionnaires were used by experts in this field.Results and discussionIn the first part, after determining the components, criteria and indicators affecting the intelligence of cities and in line with the main objectives of the research, determining the importance and weight of each component, criteria and indicators. Finally, 30 criteria with 100 indicators were finalized and these indicators were prioritized by experts. The data were analyzed by SPSS software using Friedman test and the significance of each criterion and index was determined and prioritized. The three components of smart mobility, smart governance, and smart economy with weights of 0.01577, 0.01394 and 0.01381 are the first to third priority, respectively, and have the highest weight and importance in smart cities. The criteria for smart mobility component include technology infrastructure, national, local access and sustainable mobility, which are prioritized with weights 0.01657, 0.01619, 0.01609 and 0.01424, respectively. Two criteria of smart governance components including public-social services and transparent governance with weights of 0.01636 and 0.01153 were significant. Also the criteria of smart economy component were international interactions, productivity, innovation, entrepreneurship, economic image of the city and labor market flexibility with weights 0.01601, 0.01597, 0.01544, 0.01455, 0.01161 and 0.00928 respectively. In the analytical analysis of the findings, the indicators of Internet penetration, municipality planning strategy and e-government access have the weights of 0.01694, 0.01687 and 0.01684, respectively.In the second part, we have used Micmac software to analyze the interaction of criteria. In order to make the results more realistic and realistic, the analysis software was created in the Micmac Matrix software and adjusted in 30 different criteria to 30 * 30 dimensions. Based on the matrix output, the 10 criteria that had the most direct impact on the system in order of rating are: 1- Technology infrastructure (677) 2- Citizens empowerment (655) 3- Transparent governance (610) 4- Citizen participation (519) 5- Cultural facilities (440) 6- Sustainable and safe transportation (440) 7- Lifelong learning (429) 8- Tourism attraction (406) 9- Educational facilities (395) 10- National access (395). The 10 criteria that have a direct impact on city intelligence are, respectively, priority and priority; 1- Citizen competence and competence (542) 2- City pollution level (508) 3- Urban productivity (497) 4- Entrepreneurship in the city (497) 5- Transparent Governance (463) 6- City Tourism Attraction (440) 7- International Interaction (440) 8- Preservation of Environment (418) 9- Educational Facilities (384) 10- Citizen Participation (384). ConclusionThe analysis of the results of the first section showed that the relevant components, criteria and indices in the three prioritization of the rankings were almost identical. Thus, the component and criterion that is ranked higher in the ranking also have more important indicators, while the component and the criterion that is in the lower priorities have the least important ones. In the second stage, the analysis of the interaction of criteria on the intelligence of the city is examined. The results of the MicMac method in impact analysis indicate the irregular distribution of criteria in the impact plan. The analysis of the plan and related diagrams show that the variables in the medium of influence are high density and the studied system is unstable. Based on the results of Structural Analysis and Micmac software, the direct and indirect effective criteria were determined. Key variables and strategic indicators were also identified. What is important is that improvement and improvement in each of the dimensions and components of the smart city will be very influential in city intelligence because systematic reviews and evaluations of smart cities do not consider growth in all dimensions. Therefore, any benchmark and component that can be upgraded should be a priority. |