شناسایی و تبیین موانع ساختاری و رفتاری تحقق نوآوری استراتژیک در مدیریت هوشمند و یکپارچه شهری در ایران

Extended Abstract

Introduction

Urbanization is occurring at an unprecedented rate across the globe, posing a major challenge to efforts towards sustainable development. This necessitates innovative urban design models that integrate economic growth and social justice while protecting the environment. Sustainable urban design has become a model for managing and balancing the multiple dimensions of sustainability, including environmental health, economic viability, and social equity. This strategy encompasses several stages. An inseparable part of successful governance is management, which plays a vital role in implementing strategic actions and monitoring progress. For this reason, strategic innovation needs to enter the field in order to resolve many of the existing issues and problems. Strategic innovation is the creation of value using relevant knowledge and resources to transform an idea into a new product, process, or practice that has the potential to create a major transformative effect on the evolution of markets and industries. In response to these challenges, traditional top-down urban planning approaches are increasingly being questioned, and there is a need for innovative solutions that prioritize participatory and inclusive methods; integrate smart and low-cost technologies; and enhance the institutional capacity of local authorities.

Despite these findings from the existing literature, significant theoretical gaps still remain. First, the theoretical literature indicates a lack of integration between socio-economic dimensions, governance, education, sustainability, and infrastructure. Second, despite issues related to weaknesses in smart and integrated urban management, socio-economic inequality, digital literacy, and urban informality are among the significant problems facing Iranian cities, and these problems are not analyzed using an integrated analytical framework. It is necessary first to identify the relevant obstacles and challenges, and then to carry out modeling and develop related patterns. Moreover, the existing frameworks and literature appear to be largely context-specific and sectoral. Existing frameworks typically follow one of two paths: either advanced models exported from developed countries that rely on strong infrastructure, or sector-specific models that focus on isolated urban functions. Consequently, the existing theoretical literature indicates that the need to identify and explain the structural and behavioral barriers to the realization of strategic innovation in smart and integrated urban management in Iran is strongly felt.

This fragmentation creates a problem that must be addressed. There appears to be a need to develop a comprehensive synthesis of the existing literature that identifies and explains these barriers and advances a comprehensive and complete conceptual framework. Accordingly, the main objective of the present study is to identify and explain the structural and behavioral barriers to the realization of strategic innovation in smart and integrated urban management in Iran, which have been examined from various perspectives and approaches.



Methodology

Research differs in terms of type, nature, method, and so on. The present research is applied in nature and qualitatively oriented. The research method employed is thematic analysis. Thematic analysis is one of the most widely used methods for analyzing qualitative data, offering a structured yet flexible approach to identifying, analyzing, and reporting patterns or themes within a dataset.

The research population of the present study consists of specialists in the fields of tourism management, strategic management, urban management and planning, and development management. Participants were selected using criterion-based purposive sampling, taking into account the criterion of theoretical saturation. A total of 17 experts participated in the interview process. In the fifteenth interview, the points and remarks of the experts were similar to one another; however, to observe the principle of precision and accuracy, the process continued for two more interviews. In the sixteenth and seventeenth interviews, all responses became similar, and in accordance with the principle of theoretical saturation, the interview process was discontinued.



Results and discussion

In this research, the six-step approach of Braun and Clarke was used. Familiarization with the data is a fundamental stage in thematic analysis, where researchers immerse themselves deeply in their dataset to develop an initial understanding of the breadth, depth, and patterns of the data. According to Braun and Clarke, this phase is crucial for establishing an intimate relationship between the researcher and the data, as meaningful insights cannot emerge without such engagement. The second phase of thematic analysis, generating initial codes, marks the transition from familiarization with the dataset to the systematic identification of meaningful data segments. In this phase, researchers carefully examine the dataset to generate initial codes—labels that capture important features of the data relevant to the research question. The third phase of thematic analysis, searching for themes, represents a pivotal transition from generating initial codes to identifying broader patterns or themes within the dataset. Themes represent meaningful and recurring patterns derived from codes that are aligned with the research question. The fourth phase of thematic analysis, reviewing themes, involves the critical and iterative evaluation of the potential themes identified in the previous phase. In this phase, researchers assess whether the candidate themes work in relation to the coded data extracts and the entire dataset. This phase is not merely about refining or changing themes, but about ensuring that they accurately represent the data and tell a coherent and meaningful story about the research question. The fifth phase of thematic analysis, defining and naming themes, focuses on refining and finalizing the themes identified in the previous phase to ensure that they accurately represent the dataset and effectively address the research question. This phase emphasizes the development of clear, distinct, and compelling definitions for each theme, along with meaningful names that capture their essence. The sixth and final phase of thematic analysis, producing the report, focuses on translating the refined themes into a coherent, engaging, and clear narrative. In this phase, researchers synthesize their findings into a structured document that clearly communicates the results of the thematic analysis to their intended audience. The aim is to present the themes in a way that both aligns with the research question and captures the richness and complexity of the dataset. This phase requires researchers to combine themes, supporting data extracts, and analytical insights into a meaningful n

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