Presenting an Affective Model of Identity in Urban Space (Case of Study: Neighborhoods of Tehran in 2018-2019)
The present study aims to provide a model that influences identity in the urban space in Tehran, with survey method. The data collection technique is a questionnaire designed by a researcher in the form of a sum of Likert scores, the validity of which has been confirmed by two forms, CVI and CVR. The statistical population for this study includes all citizens living in Tehran, who were selected using Cochran's 400 people formula as the multi-stage cluster sample. The theoretical framework of this research is also inspired by the theories of Berger, Goffman and Lefebvre. The results showed that the participation and interaction of the residents of the neighborhood, the sense of belonging to the neighborhood, the access to urban facilities and the attention to the environmental indicators of the neighborhood are related to the traditional identity of the residents of the neighborhood of Tehran and there is no relationship between presence in urban social life spaces and traditional identity. There is also a relationship between access to urban facilities and presence in social urban spaces with a modern identity. But there is no relationship between the participation and interaction of the residents of the neighborhood, the sense of belonging to the neighborhood and the attention to the environmental indicators in the place with the modern identity. Moreover, the structural model obtained using the AMOS software indicates the correct fit of the model, that the factors studied in the model in the form of independent variables are approximately explain 28% of the traditional identity and 45% of the modern identity.
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Investigating the role of individual tendencies as an agency Women's Economic Empowerment in Tehran
Nasraneh Zolfaghari *, , Mehdi Mokhtarpour
women s studies (Sociological & Psychological), -
Sociological Analysis of the Social and Economic Consequences of Fake Divorce, Lived Experience of Formally Divorced Social Security Pensioners Women
Narges Asgarkhani, Zahra Zare*, Sorush Fathi
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